Overview

Brought to you by YData

Dataset statistics

Number of variables68
Number of observations3933955
Missing cells0
Missing cells (%)0.0%
Duplicate rows1435
Duplicate rows (%)< 0.1%
Total size in memory761.9 MiB
Average record size in memory203.1 B

Variable types

Categorical53
Numeric11
Text4

Alerts

Dataset has 1435 (< 0.1%) duplicate rowsDuplicates
NO_MUNICIPIO_ESC has a high cardinality: 5262 distinct values High cardinality
NO_MUNICIPIO_PROVA has a high cardinality: 1715 distinct values High cardinality
NU_DESEMPENHO is highly overall correlated with NU_MEDIA_GERAL and 21 other fieldsHigh correlation
NU_INFRAESTRUTURA is highly overall correlated with Q006 and 2 other fieldsHigh correlation
NU_MEDIA_GERAL is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_CH is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_CN is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_COMP1 is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_COMP2 is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_COMP3 is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_COMP4 is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_COMP5 is highly overall correlated with NU_DESEMPENHO and 15 other fieldsHigh correlation
NU_NOTA_LC is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_MT is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
NU_NOTA_REDACAO is highly overall correlated with NU_DESEMPENHO and 16 other fieldsHigh correlation
Q006 is highly overall correlated with NU_INFRAESTRUTURA and 1 other fieldsHigh correlation
Q018 is highly overall correlated with NU_INFRAESTRUTURA and 1 other fieldsHigh correlation
Q022 is highly overall correlated with NU_INFRAESTRUTURAHigh correlation
SG_UF_ESC is highly overall correlated with TP_DEPENDENCIA_ADM_ESC and 2 other fieldsHigh correlation
TP_ANO_CONCLUIU is highly overall correlated with TP_ST_CONCLUSAOHigh correlation
TP_DEPENDENCIA_ADM_ESC is highly overall correlated with SG_UF_ESC and 2 other fieldsHigh correlation
TP_ENSINO is highly overall correlated with SG_UF_ESC and 3 other fieldsHigh correlation
TP_FAIXA_ETARIA is highly overall correlated with TP_ST_CONCLUSAOHigh correlation
TP_LOCALIZACAO_ESC is highly overall correlated with SG_UF_ESC and 3 other fieldsHigh correlation
TP_PRESENCA_CH is highly overall correlated with NU_DESEMPENHO and 21 other fieldsHigh correlation
TP_PRESENCA_CN is highly overall correlated with NU_DESEMPENHO and 21 other fieldsHigh correlation
TP_PRESENCA_GERAL is highly overall correlated with NU_DESEMPENHO and 20 other fieldsHigh correlation
TP_PRESENCA_LC is highly overall correlated with NU_DESEMPENHO and 21 other fieldsHigh correlation
TP_PRESENCA_MT is highly overall correlated with NU_DESEMPENHO and 21 other fieldsHigh correlation
TP_PRESENCA_REDACAO is highly overall correlated with NU_DESEMPENHO and 21 other fieldsHigh correlation
TP_STATUS_REDACAO is highly overall correlated with NU_DESEMPENHO and 6 other fieldsHigh correlation
TP_ST_CONCLUSAO is highly overall correlated with TP_ANO_CONCLUIU and 3 other fieldsHigh correlation
TX_GABARITO_CH is highly overall correlated with NU_DESEMPENHO and 9 other fieldsHigh correlation
TX_GABARITO_CN is highly overall correlated with NU_DESEMPENHO and 9 other fieldsHigh correlation
TX_GABARITO_LC is highly overall correlated with NU_DESEMPENHO and 9 other fieldsHigh correlation
TX_GABARITO_MT is highly overall correlated with NU_DESEMPENHO and 9 other fieldsHigh correlation
TP_ESTADO_CIVIL is highly imbalanced (70.8%) Imbalance
TP_NACIONALIDADE is highly imbalanced (92.2%) Imbalance
NO_MUNICIPIO_ESC is highly imbalanced (74.3%) Imbalance
SG_UF_ESC is highly imbalanced (62.1%) Imbalance
TP_DEPENDENCIA_ADM_ESC is highly imbalanced (53.6%) Imbalance
TP_STATUS_REDACAO is highly imbalanced (65.3%) Imbalance
Q007 is highly imbalanced (73.9%) Imbalance
Q011 is highly imbalanced (59.7%) Imbalance
Q012 is highly imbalanced (80.4%) Imbalance
Q014 is highly imbalanced (54.7%) Imbalance
Q015 is highly imbalanced (75.9%) Imbalance
Q016 is highly imbalanced (54.4%) Imbalance
Q017 is highly imbalanced (90.7%) Imbalance
Q023 is highly imbalanced (56.3%) Imbalance
Q025 is highly imbalanced (54.6%) Imbalance
NU_NOTA_COMP1 has 118035 (3.0%) zeros Zeros
NU_NOTA_COMP2 has 117829 (3.0%) zeros Zeros
NU_NOTA_COMP3 has 118280 (3.0%) zeros Zeros
NU_NOTA_COMP4 has 118340 (3.0%) zeros Zeros
NU_NOTA_COMP5 has 290015 (7.4%) zeros Zeros
NU_NOTA_REDACAO has 117829 (3.0%) zeros Zeros

Reproduction

Analysis started2025-04-15 18:32:45.484750
Analysis finished2025-04-15 19:05:00.146392
Duration32 minutes and 14.66 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

TP_FAIXA_ETARIA
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
905047 
2
753800 
4
431592 
1
347434 
5
267383 
Other values (15)
1228699 

Length

Max length2
Median length1
Mean length1.1790163
Min length1

Characters and Unicode

Total characters4638197
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14
2nd row12
3rd row6
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 905047
23.0%
2 753800
19.2%
4 431592
11.0%
1 347434
 
8.8%
5 267383
 
6.8%
11 246292
 
6.3%
6 183401
 
4.7%
7 137884
 
3.5%
12 133381
 
3.4%
8 111813
 
2.8%
Other values (10) 415928
10.6%

Length

2025-04-15T16:05:00.531283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 905047
23.0%
2 753800
19.2%
4 431592
11.0%
1 347434
 
8.8%
5 267383
 
6.8%
11 246292
 
6.3%
6 183401
 
4.7%
7 137884
 
3.5%
12 133381
 
3.4%
8 111813
 
2.8%
Other values (10) 415928
10.6%

Most occurring characters

ValueCountFrequency (%)
1 1297101
28.0%
3 1001974
21.6%
2 888048
19.1%
4 498727
 
10.8%
5 308174
 
6.6%
6 208020
 
4.5%
7 151323
 
3.3%
8 117316
 
2.5%
9 93520
 
2.0%
0 73994
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4638197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1297101
28.0%
3 1001974
21.6%
2 888048
19.1%
4 498727
 
10.8%
5 308174
 
6.6%
6 208020
 
4.5%
7 151323
 
3.3%
8 117316
 
2.5%
9 93520
 
2.0%
0 73994
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4638197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1297101
28.0%
3 1001974
21.6%
2 888048
19.1%
4 498727
 
10.8%
5 308174
 
6.6%
6 208020
 
4.5%
7 151323
 
3.3%
8 117316
 
2.5%
9 93520
 
2.0%
0 73994
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4638197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1297101
28.0%
3 1001974
21.6%
2 888048
19.1%
4 498727
 
10.8%
5 308174
 
6.6%
6 208020
 
4.5%
7 151323
 
3.3%
8 117316
 
2.5%
9 93520
 
2.0%
0 73994
 
1.6%

TP_SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
F
2411185 
M
1522770 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 2411185
61.3%
M 1522770
38.7%

Length

2025-04-15T16:05:00.858406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:01.071875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f 2411185
61.3%
m 1522770
38.7%

Most occurring characters

ValueCountFrequency (%)
F 2411185
61.3%
M 1522770
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2411185
61.3%
M 1522770
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2411185
61.3%
M 1522770
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2411185
61.3%
M 1522770
38.7%

TP_ESTADO_CIVIL
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
3491857 
2
 
200456
0
 
171900
3
 
64933
4
 
4809

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3491857
88.8%
2 200456
 
5.1%
0 171900
 
4.4%
3 64933
 
1.7%
4 4809
 
0.1%

Length

2025-04-15T16:05:01.230391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:01.417026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3491857
88.8%
2 200456
 
5.1%
0 171900
 
4.4%
3 64933
 
1.7%
4 4809
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 3491857
88.8%
2 200456
 
5.1%
0 171900
 
4.4%
3 64933
 
1.7%
4 4809
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3491857
88.8%
2 200456
 
5.1%
0 171900
 
4.4%
3 64933
 
1.7%
4 4809
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3491857
88.8%
2 200456
 
5.1%
0 171900
 
4.4%
3 64933
 
1.7%
4 4809
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3491857
88.8%
2 200456
 
5.1%
0 171900
 
4.4%
3 64933
 
1.7%
4 4809
 
0.1%

TP_COR_RACA
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
1706798 
1
1575848 
2
509511 
4
 
64512
0
 
52575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1706798
43.4%
1 1575848
40.1%
2 509511
 
13.0%
4 64512
 
1.6%
0 52575
 
1.3%
5 24711
 
0.6%

Length

2025-04-15T16:05:01.692273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:01.900428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1706798
43.4%
1 1575848
40.1%
2 509511
 
13.0%
4 64512
 
1.6%
0 52575
 
1.3%
5 24711
 
0.6%

Most occurring characters

ValueCountFrequency (%)
3 1706798
43.4%
1 1575848
40.1%
2 509511
 
13.0%
4 64512
 
1.6%
0 52575
 
1.3%
5 24711
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1706798
43.4%
1 1575848
40.1%
2 509511
 
13.0%
4 64512
 
1.6%
0 52575
 
1.3%
5 24711
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1706798
43.4%
1 1575848
40.1%
2 509511
 
13.0%
4 64512
 
1.6%
0 52575
 
1.3%
5 24711
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1706798
43.4%
1 1575848
40.1%
2 509511
 
13.0%
4 64512
 
1.6%
0 52575
 
1.3%
5 24711
 
0.6%

TP_NACIONALIDADE
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
3842681 
2
 
73429
4
 
8580
3
 
7112
0
 
2153

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3842681
97.7%
2 73429
 
1.9%
4 8580
 
0.2%
3 7112
 
0.2%
0 2153
 
0.1%

Length

2025-04-15T16:05:02.128357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:02.317378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3842681
97.7%
2 73429
 
1.9%
4 8580
 
0.2%
3 7112
 
0.2%
0 2153
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 3842681
97.7%
2 73429
 
1.9%
4 8580
 
0.2%
3 7112
 
0.2%
0 2153
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3842681
97.7%
2 73429
 
1.9%
4 8580
 
0.2%
3 7112
 
0.2%
0 2153
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3842681
97.7%
2 73429
 
1.9%
4 8580
 
0.2%
3 7112
 
0.2%
0 2153
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3842681
97.7%
2 73429
 
1.9%
4 8580
 
0.2%
3 7112
 
0.2%
0 2153
 
0.1%

TP_ST_CONCLUSAO
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
1895301 
2
1401164 
3
620067 
4
 
17423

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 1895301
48.2%
2 1401164
35.6%
3 620067
 
15.8%
4 17423
 
0.4%

Length

2025-04-15T16:05:02.560012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:02.736316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1895301
48.2%
2 1401164
35.6%
3 620067
 
15.8%
4 17423
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 1895301
48.2%
2 1401164
35.6%
3 620067
 
15.8%
4 17423
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1895301
48.2%
2 1401164
35.6%
3 620067
 
15.8%
4 17423
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1895301
48.2%
2 1401164
35.6%
3 620067
 
15.8%
4 17423
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1895301
48.2%
2 1401164
35.6%
3 620067
 
15.8%
4 17423
 
0.4%

TP_ANO_CONCLUIU
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
2243134 
1
418530 
2
264183 
17
 
167321
3
 
149798
Other values (13)
690989 

Length

Max length2
Median length1
Mean length1.0928539
Min length1

Characters and Unicode

Total characters4299238
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17
2nd row16
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2243134
57.0%
1 418530
 
10.6%
2 264183
 
6.7%
17 167321
 
4.3%
3 149798
 
3.8%
4 136449
 
3.5%
5 104195
 
2.6%
6 85411
 
2.2%
7 65549
 
1.7%
8 54769
 
1.4%
Other values (8) 244616
 
6.2%

Length

2025-04-15T16:05:02.994382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 2243134
57.0%
1 418530
 
10.6%
2 264183
 
6.7%
17 167321
 
4.3%
3 149798
 
3.8%
4 136449
 
3.5%
5 104195
 
2.6%
6 85411
 
2.2%
7 65549
 
1.7%
8 54769
 
1.4%
Other values (8) 244616
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 2283054
53.1%
1 819325
 
19.1%
2 293184
 
6.8%
7 232870
 
5.4%
3 177219
 
4.1%
4 161088
 
3.7%
5 125193
 
2.9%
6 105882
 
2.5%
8 54769
 
1.3%
9 46654
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4299238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2283054
53.1%
1 819325
 
19.1%
2 293184
 
6.8%
7 232870
 
5.4%
3 177219
 
4.1%
4 161088
 
3.7%
5 125193
 
2.9%
6 105882
 
2.5%
8 54769
 
1.3%
9 46654
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4299238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2283054
53.1%
1 819325
 
19.1%
2 293184
 
6.8%
7 232870
 
5.4%
3 177219
 
4.1%
4 161088
 
3.7%
5 125193
 
2.9%
6 105882
 
2.5%
8 54769
 
1.3%
9 46654
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4299238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2283054
53.1%
1 819325
 
19.1%
2 293184
 
6.8%
7 232870
 
5.4%
3 177219
 
4.1%
4 161088
 
3.7%
5 125193
 
2.9%
6 105882
 
2.5%
8 54769
 
1.3%
9 46654
 
1.1%

TP_ENSINO
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
-1.0
2594874 
1.0
1332195 
2.0
 
6886

Length

Max length4
Median length4
Mean length3.6596095
Min length3

Characters and Unicode

Total characters14396739
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
-1.0 2594874
66.0%
1.0 1332195
33.9%
2.0 6886
 
0.2%

Length

2025-04-15T16:05:03.233014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:03.400158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3927069
99.8%
2.0 6886
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 3933955
27.3%
0 3933955
27.3%
1 3927069
27.3%
- 2594874
18.0%
2 6886
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14396739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3933955
27.3%
0 3933955
27.3%
1 3927069
27.3%
- 2594874
18.0%
2 6886
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14396739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3933955
27.3%
0 3933955
27.3%
1 3927069
27.3%
- 2594874
18.0%
2 6886
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14396739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3933955
27.3%
0 3933955
27.3%
1 3927069
27.3%
- 2594874
18.0%
2 6886
 
< 0.1%

NO_MUNICIPIO_ESC
Categorical

High cardinality  Imbalance 

Distinct5262
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
Desconhecido
2975449 
São Paulo
 
45215
Fortaleza
 
27634
Rio de Janeiro
 
26064
Brasília
 
20113
Other values (5257)
839480 

Length

Max length32
Median length12
Mean length11.540874
Min length3

Characters and Unicode

Total characters45401280
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)< 0.1%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowDesconhecido
4th rowFortaleza
5th rowQuixadá

Common Values

ValueCountFrequency (%)
Desconhecido 2975449
75.6%
São Paulo 45215
 
1.1%
Fortaleza 27634
 
0.7%
Rio de Janeiro 26064
 
0.7%
Brasília 20113
 
0.5%
Manaus 15500
 
0.4%
Curitiba 11491
 
0.3%
Goiânia 10835
 
0.3%
Belo Horizonte 10302
 
0.3%
Salvador 9727
 
0.2%
Other values (5252) 781625
 
19.9%

Length

2025-04-15T16:05:03.656936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
desconhecido 2975449
65.8%
são 96766
 
2.1%
de 61600
 
1.4%
do 56022
 
1.2%
paulo 46493
 
1.0%
rio 41612
 
0.9%
fortaleza 27741
 
0.6%
janeiro 26064
 
0.6%
brasília 20194
 
0.4%
manaus 15500
 
0.3%
Other values (3941) 1151863
 
25.5%

Most occurring characters

ValueCountFrequency (%)
o 6867280
15.1%
e 6532215
14.4%
c 6095657
13.4%
i 3635911
8.0%
n 3415775
7.5%
s 3326284
7.3%
d 3278538
7.2%
h 3038860
6.7%
D 2991657
6.6%
a 1418000
 
3.1%
Other values (61) 4801103
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45401280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6867280
15.1%
e 6532215
14.4%
c 6095657
13.4%
i 3635911
8.0%
n 3415775
7.5%
s 3326284
7.3%
d 3278538
7.2%
h 3038860
6.7%
D 2991657
6.6%
a 1418000
 
3.1%
Other values (61) 4801103
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45401280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6867280
15.1%
e 6532215
14.4%
c 6095657
13.4%
i 3635911
8.0%
n 3415775
7.5%
s 3326284
7.3%
d 3278538
7.2%
h 3038860
6.7%
D 2991657
6.6%
a 1418000
 
3.1%
Other values (61) 4801103
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45401280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6867280
15.1%
e 6532215
14.4%
c 6095657
13.4%
i 3635911
8.0%
n 3415775
7.5%
s 3326284
7.3%
d 3278538
7.2%
h 3038860
6.7%
D 2991657
6.6%
a 1418000
 
3.1%
Other values (61) 4801103
10.6%

SG_UF_ESC
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Desconhecido
2975449 
SP
 
187067
CE
 
98595
MG
 
66972
RJ
 
63842
Other values (23)
542030 

Length

Max length12
Median length12
Mean length9.5635054
Min length2

Characters and Unicode

Total characters37622400
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowDesconhecido
4th rowCE
5th rowCE

Common Values

ValueCountFrequency (%)
Desconhecido 2975449
75.6%
SP 187067
 
4.8%
CE 98595
 
2.5%
MG 66972
 
1.7%
RJ 63842
 
1.6%
PR 54226
 
1.4%
GO 45796
 
1.2%
PE 44881
 
1.1%
BA 43453
 
1.1%
RS 39700
 
1.0%
Other values (18) 313974
 
8.0%

Length

2025-04-15T16:05:03.906160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
desconhecido 2975449
75.6%
sp 187067
 
4.8%
ce 98595
 
2.5%
mg 66972
 
1.7%
rj 63842
 
1.6%
pr 54226
 
1.4%
go 45796
 
1.2%
pe 44881
 
1.1%
ba 43453
 
1.1%
rs 39700
 
1.0%
Other values (18) 313974
 
8.0%

Most occurring characters

ValueCountFrequency (%)
e 5950898
15.8%
o 5950898
15.8%
c 5950898
15.8%
D 2995562
8.0%
s 2975449
7.9%
n 2975449
7.9%
h 2975449
7.9%
i 2975449
7.9%
d 2975449
7.9%
P 368889
 
1.0%
Other values (15) 1528010
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37622400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5950898
15.8%
o 5950898
15.8%
c 5950898
15.8%
D 2995562
8.0%
s 2975449
7.9%
n 2975449
7.9%
h 2975449
7.9%
i 2975449
7.9%
d 2975449
7.9%
P 368889
 
1.0%
Other values (15) 1528010
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37622400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5950898
15.8%
o 5950898
15.8%
c 5950898
15.8%
D 2995562
8.0%
s 2975449
7.9%
n 2975449
7.9%
h 2975449
7.9%
i 2975449
7.9%
d 2975449
7.9%
P 368889
 
1.0%
Other values (15) 1528010
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37622400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5950898
15.8%
o 5950898
15.8%
c 5950898
15.8%
D 2995562
8.0%
s 2975449
7.9%
n 2975449
7.9%
h 2975449
7.9%
i 2975449
7.9%
d 2975449
7.9%
P 368889
 
1.0%
Other values (15) 1528010
 
4.1%

TP_DEPENDENCIA_ADM_ESC
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
-1.0
2975449 
2.0
671488 
4.0
 
229628
1.0
 
48433
3.0
 
8957

Length

Max length4
Median length4
Mean length3.7563505
Min length3

Characters and Unicode

Total characters14777314
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
-1.0 2975449
75.6%
2.0 671488
 
17.1%
4.0 229628
 
5.8%
1.0 48433
 
1.2%
3.0 8957
 
0.2%

Length

2025-04-15T16:05:04.160763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:04.348969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3023882
76.9%
2.0 671488
 
17.1%
4.0 229628
 
5.8%
3.0 8957
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3023882
20.5%
- 2975449
20.1%
2 671488
 
4.5%
4 229628
 
1.6%
3 8957
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14777314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3023882
20.5%
- 2975449
20.1%
2 671488
 
4.5%
4 229628
 
1.6%
3 8957
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14777314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3023882
20.5%
- 2975449
20.1%
2 671488
 
4.5%
4 229628
 
1.6%
3 8957
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14777314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3023882
20.5%
- 2975449
20.1%
2 671488
 
4.5%
4 229628
 
1.6%
3 8957
 
0.1%

TP_LOCALIZACAO_ESC
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
-1.0
2975449 
1.0
922856 
2.0
 
35650

Length

Max length4
Median length4
Mean length3.7563505
Min length3

Characters and Unicode

Total characters14777314
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
-1.0 2975449
75.6%
1.0 922856
 
23.5%
2.0 35650
 
0.9%

Length

2025-04-15T16:05:04.630479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:04.853772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3898305
99.1%
2.0 35650
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3898305
26.4%
- 2975449
20.1%
2 35650
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14777314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3898305
26.4%
- 2975449
20.1%
2 35650
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14777314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3898305
26.4%
- 2975449
20.1%
2 35650
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14777314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3933955
26.6%
0 3933955
26.6%
1 3898305
26.4%
- 2975449
20.1%
2 35650
 
0.2%

NO_MUNICIPIO_PROVA
Categorical

High cardinality 

Distinct1715
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
São Paulo
 
157220
Rio de Janeiro
 
116997
Fortaleza
 
74112
Brasília
 
72975
Salvador
 
69514
Other values (1710)
3443137 

Length

Max length30
Median length26
Mean length9.981362
Min length3

Characters and Unicode

Total characters39266229
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrasília
2nd rowBrasília
3rd rowCaxias do Sul
4th rowFortaleza
5th rowQuixadá

Common Values

ValueCountFrequency (%)
São Paulo 157220
 
4.0%
Rio de Janeiro 116997
 
3.0%
Fortaleza 74112
 
1.9%
Brasília 72975
 
1.9%
Salvador 69514
 
1.8%
Belém 63314
 
1.6%
Manaus 62200
 
1.6%
Belo Horizonte 57959
 
1.5%
São Luís 49133
 
1.2%
Recife 47361
 
1.2%
Other values (1705) 3163170
80.4%

Length

2025-04-15T16:05:05.067267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
são 385129
 
6.1%
de 270227
 
4.3%
do 196715
 
3.1%
rio 184374
 
2.9%
paulo 161126
 
2.6%
janeiro 116997
 
1.9%
fortaleza 74112
 
1.2%
brasília 74077
 
1.2%
salvador 69514
 
1.1%
belém 64319
 
1.0%
Other values (1622) 4697765
74.6%

Most occurring characters

ValueCountFrequency (%)
a 5716872
14.6%
o 3685905
 
9.4%
r 2719057
 
6.9%
i 2622926
 
6.7%
e 2453652
 
6.2%
2360400
 
6.0%
n 1815726
 
4.6%
s 1494293
 
3.8%
u 1436309
 
3.7%
t 1312363
 
3.3%
Other values (56) 13648726
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39266229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5716872
14.6%
o 3685905
 
9.4%
r 2719057
 
6.9%
i 2622926
 
6.7%
e 2453652
 
6.2%
2360400
 
6.0%
n 1815726
 
4.6%
s 1494293
 
3.8%
u 1436309
 
3.7%
t 1312363
 
3.3%
Other values (56) 13648726
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39266229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5716872
14.6%
o 3685905
 
9.4%
r 2719057
 
6.9%
i 2622926
 
6.7%
e 2453652
 
6.2%
2360400
 
6.0%
n 1815726
 
4.6%
s 1494293
 
3.8%
u 1436309
 
3.7%
t 1312363
 
3.3%
Other values (56) 13648726
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39266229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5716872
14.6%
o 3685905
 
9.4%
r 2719057
 
6.9%
i 2622926
 
6.7%
e 2453652
 
6.2%
2360400
 
6.0%
n 1815726
 
4.6%
s 1494293
 
3.8%
u 1436309
 
3.7%
t 1312363
 
3.3%
Other values (56) 13648726
34.8%

SG_UF_PROVA
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
SP
590767 
MG
358575 
BA
324268 
RJ
282296 
CE
241960 
Other values (22)
2136089 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7867910
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDF
2nd rowDF
3rd rowRS
4th rowCE
5th rowCE

Common Values

ValueCountFrequency (%)
SP 590767
15.0%
MG 358575
 
9.1%
BA 324268
 
8.2%
RJ 282296
 
7.2%
CE 241960
 
6.2%
PA 229162
 
5.8%
PE 218859
 
5.6%
PR 166506
 
4.2%
MA 165756
 
4.2%
RS 159919
 
4.1%
Other values (17) 1195887
30.4%

Length

2025-04-15T16:05:05.347036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 590767
15.0%
mg 358575
 
9.1%
ba 324268
 
8.2%
rj 282296
 
7.2%
ce 241960
 
6.2%
pa 229162
 
5.8%
pe 218859
 
5.6%
pr 166506
 
4.2%
ma 165756
 
4.2%
rs 159919
 
4.1%
Other values (17) 1195887
30.4%

Most occurring characters

ValueCountFrequency (%)
P 1458251
18.5%
S 1028668
13.1%
A 947943
12.0%
R 764743
9.7%
M 728614
9.3%
E 600083
7.6%
G 507685
 
6.5%
B 448779
 
5.7%
C 357497
 
4.5%
J 282296
 
3.6%
Other values (7) 743351
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7867910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1458251
18.5%
S 1028668
13.1%
A 947943
12.0%
R 764743
9.7%
M 728614
9.3%
E 600083
7.6%
G 507685
 
6.5%
B 448779
 
5.7%
C 357497
 
4.5%
J 282296
 
3.6%
Other values (7) 743351
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7867910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1458251
18.5%
S 1028668
13.1%
A 947943
12.0%
R 764743
9.7%
M 728614
9.3%
E 600083
7.6%
G 507685
 
6.5%
B 448779
 
5.7%
C 357497
 
4.5%
J 282296
 
3.6%
Other values (7) 743351
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7867910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1458251
18.5%
S 1028668
13.1%
A 947943
12.0%
R 764743
9.7%
M 728614
9.3%
E 600083
7.6%
G 507685
 
6.5%
B 448779
 
5.7%
C 357497
 
4.5%
J 282296
 
3.6%
Other values (7) 743351
9.4%

TP_PRESENCA_CN
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
2692427 
0
1239316 
2
 
2212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Length

2025-04-15T16:05:05.568116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:05.713897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

TP_PRESENCA_CH
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
2822643 
0
1106714 
2
 
4598

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Length

2025-04-15T16:05:05.977546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:06.234292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

TP_PRESENCA_LC
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
2822643 
0
1106714 
2
 
4598

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Length

2025-04-15T16:05:06.495429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:06.739804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1106714
 
28.1%
2 4598
 
0.1%

TP_PRESENCA_MT
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
2692427 
0
1239316 
2
 
2212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Length

2025-04-15T16:05:07.021451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:05:07.234319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2692427
68.4%
0 1239316
31.5%
2 2212
 
0.1%

NU_NOTA_CN
Real number (ℝ)

High correlation 

Distinct1426
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.98024
Minimum-1
Maximum868.5
Zeros16547
Zeros (%)0.4%
Negative1241528
Negative (%)31.6%
Memory size30.0 MiB
2025-04-15T16:05:07.432866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median445
Q3524
95-th percentile615.5
Maximum868.5
Range869.5
Interquartile range (IQR)525

Descriptive statistics

Standard deviation242.05494
Coefficient of variation (CV)0.714068
Kurtosis-1.3909753
Mean338.98024
Median Absolute Deviation (MAD)106
Skewness-0.53815357
Sum1.333533 × 109
Variance58590.594
MonotonicityNot monotonic
2025-04-15T16:05:07.817871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1241528
31.6%
0 16547
 
0.4%
513 6077
 
0.2%
512.5 6061
 
0.2%
518 6025
 
0.2%
515.5 6018
 
0.2%
515 6013
 
0.2%
516.5 6003
 
0.2%
521 5979
 
0.2%
513.5 5976
 
0.2%
Other values (1416) 2627728
66.8%
ValueCountFrequency (%)
-1 1241528
31.6%
0 16547
 
0.4%
316 1
 
< 0.1%
318.25 1
 
< 0.1%
320 1
 
< 0.1%
320.25 1
 
< 0.1%
320.75 1
 
< 0.1%
321 1
 
< 0.1%
321.75 1
 
< 0.1%
322.5 3
 
< 0.1%
ValueCountFrequency (%)
868.5 14
< 0.1%
856.5 11
< 0.1%
854.5 9
< 0.1%
854 20
< 0.1%
850.5 2
 
< 0.1%
847 3
 
< 0.1%
844.5 16
< 0.1%
844 4
 
< 0.1%
843.5 21
< 0.1%
843 2
 
< 0.1%

NU_NOTA_CH
Real number (ℝ)

High correlation 

Distinct1437
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.22821
Minimum-1
Maximum823
Zeros5612
Zeros (%)0.1%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:05:08.194567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median483.75
Q3562.5
95-th percentile644.5
Maximum823
Range824
Interquartile range (IQR)563.5

Descriptive statistics

Standard deviation247.70538
Coefficient of variation (CV)0.66014593
Kurtosis-1.1981286
Mean375.22821
Median Absolute Deviation (MAD)104
Skewness-0.69097851
Sum1.4761309 × 109
Variance61357.954
MonotonicityNot monotonic
2025-04-15T16:05:08.457174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1111312
28.2%
540.5 6888
 
0.2%
537.5 6864
 
0.2%
547.5 6798
 
0.2%
547 6789
 
0.2%
538.5 6768
 
0.2%
541 6757
 
0.2%
536.5 6757
 
0.2%
533 6736
 
0.2%
542.5 6731
 
0.2%
Other values (1427) 2761555
70.2%
ValueCountFrequency (%)
-1 1111312
28.2%
0 5612
 
0.1%
290 3
 
< 0.1%
293.25 1
 
< 0.1%
293.5 505
 
< 0.1%
293.75 63
 
< 0.1%
294 98
 
< 0.1%
294.25 61
 
< 0.1%
294.5 133
 
< 0.1%
294.75 41
 
< 0.1%
ValueCountFrequency (%)
823 81
< 0.1%
805 34
< 0.1%
804.5 38
< 0.1%
804 34
< 0.1%
800.5 51
< 0.1%
799.5 72
< 0.1%
798.5 8
 
< 0.1%
796.5 1
 
< 0.1%
794.5 30
 
< 0.1%
794 3
 
< 0.1%

NU_NOTA_LC
Real number (ℝ)

High correlation 

Distinct1449
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371.49168
Minimum-1
Maximum821
Zeros2169
Zeros (%)0.1%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:05:08.810483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median484
Q3551
95-th percentile622
Maximum821
Range822
Interquartile range (IQR)552

Descriptive statistics

Standard deviation242.30676
Coefficient of variation (CV)0.65225354
Kurtosis-1.1706386
Mean371.49168
Median Absolute Deviation (MAD)88
Skewness-0.752394
Sum1.4614315 × 109
Variance58712.567
MonotonicityNot monotonic
2025-04-15T16:05:09.208379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1111312
28.2%
527.5 7981
 
0.2%
533 7952
 
0.2%
538.5 7896
 
0.2%
529.5 7863
 
0.2%
532 7851
 
0.2%
532.5 7846
 
0.2%
536 7840
 
0.2%
526 7834
 
0.2%
524 7833
 
0.2%
Other values (1439) 2751747
69.9%
ValueCountFrequency (%)
-1 1111312
28.2%
0 2169
 
0.1%
287 1
 
< 0.1%
287.25 185
 
< 0.1%
287.5 14
 
< 0.1%
287.75 38
 
< 0.1%
288 44
 
< 0.1%
288.25 40
 
< 0.1%
288.5 127
 
< 0.1%
288.75 59
 
< 0.1%
ValueCountFrequency (%)
821 5
< 0.1%
803 1
 
< 0.1%
801 3
< 0.1%
798.5 1
 
< 0.1%
798 1
 
< 0.1%
797.5 1
 
< 0.1%
796.5 1
 
< 0.1%
795.5 3
< 0.1%
791.5 3
< 0.1%
791 6
< 0.1%

NU_NOTA_MT
Real number (ℝ)

High correlation 

Distinct1625
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.04534
Minimum-1
Maximum958.5
Zeros16638
Zeros (%)0.4%
Negative1241528
Negative (%)31.6%
Memory size30.0 MiB
2025-04-15T16:05:09.587870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median438
Q3579
95-th percentile730
Maximum958.5
Range959.5
Interquartile range (IQR)580

Descriptive statistics

Standard deviation271.37879
Coefficient of variation (CV)0.74341119
Kurtosis-1.3428329
Mean365.04534
Median Absolute Deviation (MAD)186
Skewness-0.30995559
Sum1.4360719 × 109
Variance73646.448
MonotonicityNot monotonic
2025-04-15T16:05:09.923332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1241528
31.6%
0 16638
 
0.4%
516.5 3569
 
0.1%
515.5 3548
 
0.1%
517 3511
 
0.1%
519 3502
 
0.1%
514 3501
 
0.1%
516 3488
 
0.1%
523 3486
 
0.1%
525.5 3484
 
0.1%
Other values (1615) 2647700
67.3%
ValueCountFrequency (%)
-1 1241528
31.6%
0 16638
 
0.4%
319.75 1
 
< 0.1%
320.75 1
 
< 0.1%
321 1
 
< 0.1%
321.25 1
 
< 0.1%
321.75 1
 
< 0.1%
322 1
 
< 0.1%
322.25 1
 
< 0.1%
322.75 3
 
< 0.1%
ValueCountFrequency (%)
958.5 532
< 0.1%
948 29
 
< 0.1%
946.5 110
 
< 0.1%
945.5 43
 
< 0.1%
945 62
 
< 0.1%
943.5 268
< 0.1%
941 50
 
< 0.1%
940.5 4
 
< 0.1%
940 10
 
< 0.1%
939 69
 
< 0.1%
Distinct2675116
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
2025-04-15T16:05:17.822990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length45
Mean length34.585437
Min length12

Characters and Unicode

Total characters136057551
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2674960 ?
Unique (%)68.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowDBEBDCECCBCEBBBBDBABDDBBAABCBACDBACECCBAADEBB
4th rowDEEBEACCCEBDDBDCCCAEEDCBAAADBCBEEEDCDAAECBEEC
5th rowAECCEAACDEABEEECDBAEEAAADDEABCBCEBACEEDCBEABD
ValueCountFrequency (%)
desconhecido 1241528
31.6%
16549
 
0.4%
ccccccccccccccccccccccccccccccccccccccccccccc 124
 
< 0.1%
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 93
 
< 0.1%
ddddddddddddddddddddddddddddddddddddddddddddd 62
 
< 0.1%
bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb 53
 
< 0.1%
abcdedcbabcdedcbabcdedcbabcdedcbabcdedcbabcde 48
 
< 0.1%
abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde 42
 
< 0.1%
aaaaaaaaaaaaaaabbbbbbbbbbbbbbbccccccccccccccc 36
 
< 0.1%
eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee 26
 
< 0.1%
Other values (2675071) 2675394
68.0%
2025-04-15T16:05:22.739995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 26138572
19.2%
C 25882152
19.0%
E 24471373
18.0%
D 24072941
17.7%
A 20833324
15.3%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4726965
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 26138572
19.2%
C 25882152
19.0%
E 24471373
18.0%
D 24072941
17.7%
A 20833324
15.3%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4726965
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 26138572
19.2%
C 25882152
19.0%
E 24471373
18.0%
D 24072941
17.7%
A 20833324
15.3%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4726965
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 26138572
19.2%
C 25882152
19.0%
E 24471373
18.0%
D 24072941
17.7%
A 20833324
15.3%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4726965
 
3.5%
Distinct2814296
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
2025-04-15T16:05:30.459823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length45
Mean length35.677754
Min length12

Characters and Unicode

Total characters140354679
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2813119 ?
Unique (%)71.5%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowABDEADAADCDABDCADAEABCDDCBAADCCBEBCEBEBDBEAED
4th rowDDAAEEBCCDEADBCDDCBAECABEBDEBDABECECEDCDDAEED
5th rowCADEBCEDDEBCBAEBADDCECACADBDEBABDBDBEEDBBEADC
ValueCountFrequency (%)
desconhecido 1111312
 
28.2%
5617
 
0.1%
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 164
 
< 0.1%
ccccccccccccccccccccccccccccccccccccccccccccc 118
 
< 0.1%
a 98
 
< 0.1%
d 97
 
< 0.1%
ddddddddddddddddddddddddddddddddddddddddddddd 92
 
< 0.1%
bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb 88
 
< 0.1%
abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde 70
 
< 0.1%
b 60
 
< 0.1%
Other values (2814092) 2816239
71.6%
2025-04-15T16:05:35.523726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 30418332
21.7%
D 27700030
19.7%
C 25094560
17.9%
B 23324406
16.6%
E 20500244
14.6%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4426611
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 30418332
21.7%
D 27700030
19.7%
C 25094560
17.9%
B 23324406
16.6%
E 20500244
14.6%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4426611
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 30418332
21.7%
D 27700030
19.7%
C 25094560
17.9%
B 23324406
16.6%
E 20500244
14.6%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4426611
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 30418332
21.7%
D 27700030
19.7%
C 25094560
17.9%
B 23324406
16.6%
E 20500244
14.6%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4426611
 
3.2%
Distinct2819955
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
2025-04-15T16:05:42.709598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length45
Mean length35.677754
Min length12

Characters and Unicode

Total characters140354679
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2819762 ?
Unique (%)71.7%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowACEBDCABAACAEBAECEBBBAAECBBDEADCAECCCEDDABEED
4th rowADBDADAEEEACAABBACADCAEBBAAEBBCDEBBDDADDCADAA
5th rowAABBACBCAEDABDADEDAACCAEEEECAACDCADBAEACDEAAE
ValueCountFrequency (%)
desconhecido 1111312
 
28.2%
2173
 
0.1%
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 57
 
< 0.1%
a 50
 
< 0.1%
b 41
 
< 0.1%
abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde 36
 
< 0.1%
ccccccccccccccccccccccccccccccccccccccccccccc 33
 
< 0.1%
c 28
 
< 0.1%
ddddddddddddddddddddddddddddddddddddddddddddd 23
 
< 0.1%
abcdedcbabcdedcbabcdedcbabcdedcbabcdedcbabcde 23
 
< 0.1%
Other values (2819850) 2820179
71.7%
2025-04-15T16:05:47.979111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 30804866
21.9%
B 28189945
20.1%
C 24782250
17.7%
D 23275080
16.6%
E 20411307
14.5%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4000735
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 30804866
21.9%
B 28189945
20.1%
C 24782250
17.7%
D 23275080
16.6%
E 20411307
14.5%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4000735
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 30804866
21.9%
B 28189945
20.1%
C 24782250
17.7%
D 23275080
16.6%
E 20411307
14.5%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4000735
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 30804866
21.9%
B 28189945
20.1%
C 24782250
17.7%
D 23275080
16.6%
E 20411307
14.5%
e 2222624
 
1.6%
c 2222624
 
1.6%
o 2222624
 
1.6%
h 1111312
 
0.8%
s 1111312
 
0.8%
Other values (5) 4000735
 
2.9%
Distinct2673884
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
2025-04-15T16:05:55.154747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length45
Mean length34.585437
Min length12

Characters and Unicode

Total characters136057551
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2673336 ?
Unique (%)68.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowCEAEACCCDABCDAACEDDBAAEBABDDEEBDAECABDBCBCADE
4th rowEECBAEDEEDDDBBAADEECDBBBECEAACEAEECDBEDDBCDCB
5th rowCDBABEDCEEBBBDECDEBACCAABDEDCBECDECABBDBDEECC
ValueCountFrequency (%)
desconhecido 1241528
31.6%
16639
 
0.4%
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 104
 
< 0.1%
ccccccccccccccccccccccccccccccccccccccccccccc 100
 
< 0.1%
ddddddddddddddddddddddddddddddddddddddddddddd 80
 
< 0.1%
bccdeeabcbedceabbebdabddaddadecaaddccbebeabcc 66
 
< 0.1%
bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb 63
 
< 0.1%
dcecaccbdecbeeabeabddaaddabbbccbccddaebdadeeb 53
 
< 0.1%
ebdaddaebeacbedceccbeabcadebccbccdebddaabbadd 51
 
< 0.1%
ebdeebdaddabccbccabbaddbddaeaeabcbedecadceccb 51
 
< 0.1%
Other values (2673820) 2675220
68.0%
2025-04-15T16:05:59.758064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 28504397
21.0%
C 28057792
20.6%
B 24445807
18.0%
A 20130126
14.8%
E 20106608
14.8%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4880597
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 28504397
21.0%
C 28057792
20.6%
B 24445807
18.0%
A 20130126
14.8%
E 20106608
14.8%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4880597
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 28504397
21.0%
C 28057792
20.6%
B 24445807
18.0%
A 20130126
14.8%
E 20106608
14.8%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4880597
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 28504397
21.0%
C 28057792
20.6%
B 24445807
18.0%
A 20130126
14.8%
E 20106608
14.8%
e 2483056
 
1.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
h 1241528
 
0.9%
s 1241528
 
0.9%
Other values (5) 4880597
 
3.6%

TP_LINGUA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
2136104 
1
1797851 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2136104
54.3%
1 1797851
45.7%

Length

2025-04-15T16:05:59.954223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:00.112969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2136104
54.3%
1 1797851
45.7%

Most occurring characters

ValueCountFrequency (%)
0 2136104
54.3%
1 1797851
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2136104
54.3%
1 1797851
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2136104
54.3%
1 1797851
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2136104
54.3%
1 1797851
45.7%

TX_GABARITO_CN
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
Desconhecido
1241528 
CEDAEEDEECCEBADCCCABBABCAACDDDACDBEABDCDBEABD
678784 
CDDDABBABDBEABDECCEEEDCEDAEBABDCCAACCCADACDBE
669321 
CAAADCCCCDDDABDCACDBEEEDCEDAEECCDBEABDBABBAEB
668909 
DBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEED
668204 
Other values (6)
 
7209

Length

Max length45
Median length45
Mean length34.585437
Min length12

Characters and Unicode

Total characters136057551
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowDBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEED
4th rowCDDDABBABDBEABDECCEEEDCEDAEBABDCCAACCCADACDBE
5th rowCAAADCCCCDDDABDCACDBEEEDCEDAEECCDBEABDBABBAEB

Common Values

ValueCountFrequency (%)
Desconhecido 1241528
31.6%
CEDAEEDEECCEBADCCCABBABCAACDDDACDBEABDCDBEABD 678784
17.3%
CDDDABBABDBEABDECCEEEDCEDAEBABDCCAACCCADACDBE 669321
17.0%
CAAADCCCCDDDABDCACDBEEEDCEDAEECCDBEABDBABBAEB 668909
17.0%
DBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEED 668204
17.0%
BDBEACECBABBCDDDBEBEACCDABACEACEEACCDCDEDDCCC 1313
 
< 0.1%
ACCDAEACBACCCEEADCDEDDCCBEBEBBCCDDDEABDBCECBA 1301
 
< 0.1%
CBABDBCEEADDDBBCCBEBEEDDCCDCDEEACCBACEACACCDA 1297
 
< 0.1%
BBCDCDBACCACCDAEEAEACCBAEDDCCBDBEACCCEDDDBEBE 1293
 
< 0.1%
CEDAEEDEECCEBADCCCABBABBAACDDDACDBEABDCDBEABD 1212
 
< 0.1%

Length

2025-04-15T16:06:00.378923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
desconhecido 1241528
31.6%
cedaeedeeccebadcccabbabcaacdddacdbeabdcdbeabd 678784
17.3%
cdddabbabdbeabdecceeedcedaebabdccaacccadacdbe 669321
17.0%
caaadccccdddabdcacdbeeedcedaeeccdbeabdbabbaeb 668909
17.0%
dbeabdabdcacdbecdddbcaaabbacccadebecccedaeeed 668204
17.0%
bdbeacecbabbcdddbebeaccdabaceaceeaccdcdeddccc 1313
 
< 0.1%
accdaeacbaccceeadcdeddccbebebbccdddeabdbcecba 1301
 
< 0.1%
cbabdbceeadddbbccbebeeddccdcdeeaccbaceacaccda 1297
 
< 0.1%
bbcdcdbaccaccdaeeaeaccbaeddccbdbeacccedddbebe 1293
 
< 0.1%
cedaeedeeccebadcccabbabbaacdddacdbeabdcdbeabd 1212
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 28162180
20.7%
C 26938670
19.8%
A 24221435
17.8%
B 21539835
15.8%
E 21538623
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 28162180
20.7%
C 26938670
19.8%
A 24221435
17.8%
B 21539835
15.8%
E 21538623
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 28162180
20.7%
C 26938670
19.8%
A 24221435
17.8%
B 21539835
15.8%
E 21538623
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 28162180
20.7%
C 26938670
19.8%
A 24221435
17.8%
B 21539835
15.8%
E 21538623
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

TX_GABARITO_CH
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
Desconhecido
1111312 
ABCDCBDACDAEACEECABADBEABADEBAABCDCABADCDAADE
713419 
ACEEABAADCDAADEABCDABCDCABCBDADEBAECABADBCDAE
701519 
DBAADEADCDCABABCDDEBAEABAECABAACECDAECBDAABCD
701475 
CDAEECABAACEAADECBDAABCDCABADCDEABAABCDDEBADB
701099 
Other values (4)
 
5131

Length

Max length45
Median length45
Mean length35.677754
Min length12

Characters and Unicode

Total characters140354679
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowACEEABAADCDAADEABCDABCDCABCBDADEBAECABADBCDAE
4th rowDBAADEADCDCABABCDDEBAEABAECABAACECDAECBDAABCD
5th rowCDAEECABAACEAADECBDAABCDCABADCDEABAABCDDEBADB

Common Values

ValueCountFrequency (%)
Desconhecido 1111312
28.2%
ABCDCBDACDAEACEECABADBEABADEBAABCDCABADCDAADE 713419
18.1%
ACEEABAADCDAADEABCDABCDCABCBDADEBAECABADBCDAE 701519
17.8%
DBAADEADCDCABABCDDEBAEABAECABAACECDAECBDAABCD 701475
17.8%
CDAEECABAACEAADECBDAABCDCABADCDEABAABCDDEBADB 701099
17.8%
DCDADEBCBBACEAEDADEADAECCBDABCBCBEABCACBCABAA 1295
 
< 0.1%
BDABCECCBCABAAABCACBCBEDEBCBDCDABACEAADAEDADE 1290
 
< 0.1%
DEBCBBDABCECCBACEACABAADCDABADAEDADEABCACBCBE 1274
 
< 0.1%
BCBEABCEDADEACADABDCDACABAABACEAECCBDABCDEBCB 1272
 
< 0.1%

Length

2025-04-15T16:06:00.568329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:00.818976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
desconhecido 1111312
28.2%
abcdcbdacdaeaceecabadbeabadebaabcdcabadcdaade 713419
18.1%
aceeabaadcdaadeabcdabcdcabcbdadebaecabadbcdae 701519
17.8%
dbaadeadcdcababcddebaeabaecabaacecdaecbdaabcd 701475
17.8%
cdaeecabaaceaadecbdaabcdcabadcdeabaabcddebadb 701099
17.8%
dcdadebcbbaceaedadeadaeccbdabcbcbeabcacbcabaa 1295
 
< 0.1%
bdabceccbcabaaabcacbcbedebcbdcdabaceaadaedade 1290
 
< 0.1%
debcbbdabceccbaceacabaadcdabadaedadeabcacbcbe 1274
 
< 0.1%
bcbeabcedadeacadabdcdacabaabaceaeccbdabcdebcb 1272
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 39506740
28.1%
D 26504837
18.9%
B 22591406
16.1%
C 22591406
16.1%
E 16935858
12.1%
c 2222624
 
1.6%
o 2222624
 
1.6%
e 2222624
 
1.6%
s 1111312
 
0.8%
d 1111312
 
0.8%
Other values (3) 3333936
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 39506740
28.1%
D 26504837
18.9%
B 22591406
16.1%
C 22591406
16.1%
E 16935858
12.1%
c 2222624
 
1.6%
o 2222624
 
1.6%
e 2222624
 
1.6%
s 1111312
 
0.8%
d 1111312
 
0.8%
Other values (3) 3333936
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 39506740
28.1%
D 26504837
18.9%
B 22591406
16.1%
C 22591406
16.1%
E 16935858
12.1%
c 2222624
 
1.6%
o 2222624
 
1.6%
e 2222624
 
1.6%
s 1111312
 
0.8%
d 1111312
 
0.8%
Other values (3) 3333936
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140354679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 39506740
28.1%
D 26504837
18.9%
B 22591406
16.1%
C 22591406
16.1%
E 16935858
12.1%
c 2222624
 
1.6%
o 2222624
 
1.6%
e 2222624
 
1.6%
s 1111312
 
0.8%
d 1111312
 
0.8%
Other values (3) 3333936
 
2.4%

TX_GABARITO_LC
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
Desconhecido
1111312 
BDBBABAAAEAAECBBEAACAAACEACBCACCCEDEDADBDBEEDDACCC
712147 
DBABBAEBAAAACDACDEDAACADBADBCCEACCCEAAECBBEBCACEED
701518 
BBBDAABAEACCEEEDEACBCACAACAACAAAECBBEDBCCADBDEDDAC
701475 
BBDABAAEBADACEEDCCDBADBDEDCCEBCACEACAACAACACBBEAAE
701099 
Other values (5)
 
6404

Length

Max length50
Median length50
Mean length39.265293
Min length12

Characters and Unicode

Total characters154467894
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowDBABBAEBAAAACDACDEDAACADBADBCCEACCCEAAECBBEBCACEED
4th rowBBBDAABAEACCEEEDEACBCACAACAACAAAECBBEDBCCADBDEDDAC
5th rowBBDABAAEBADACEEDCCDBADBDEDCCEBCACEACAACAACACBBEAAE

Common Values

ValueCountFrequency (%)
Desconhecido 1111312
28.2%
BDBBABAAAEAAECBBEAACAAACEACBCACCCEDEDADBDBEEDDACCC 712147
18.1%
DBABBAEBAAAACDACDEDAACADBADBCCEACCCEAAECBBEBCACEED 701518
17.8%
BBBDAABAEACCEEEDEACBCACAACAACAAAECBBEDBCCADBDEDDAC 701475
17.8%
BBDABAAEBADACEEDCCDBADBDEDCCEBCACEACAACAACACBBEAAE 701099
17.8%
EAEDCEEDCEACBBBDACEBAECBDBADCDDEBECEEDDEDAADABADCD 1295
 
< 0.1%
ECAEDDEEECAADEEDDCDACBABAACBADBBDDEDCBDBECEBAECDDE 1290
 
< 0.1%
DEECAEDECECBDDEDBECEEDAADDCDACBABABADACEBAECDDEBBD 1274
 
< 0.1%
BDBBABAAAEAAECBBEAACAEACEACBCACCCEDEDADBEBEEDDACCC 1273
 
< 0.1%
AEDECECDEECDDEEBAEBECDEDCBDBBDACABABADACBDCDEEDAAD 1272
 
< 0.1%

Length

2025-04-15T16:06:01.117293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:01.306104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
desconhecido 1111312
28.2%
bdbbabaaaeaaecbbeaacaaaceacbcacccededadbdbeeddaccc 712147
18.1%
dbabbaebaaaacdacdedaacadbadbcceaccceaaecbbebcaceed 701518
17.8%
bbbdaabaeacceeedeacbcacaacaacaaaecbbedbccadbdeddac 701475
17.8%
bbdabaaebadaceedccdbadbdedccebcaceacaacaacacbbeaae 701099
17.8%
eaedceedceacbbbdacebaecbdbadcddebeceeddedaadabadcd 1295
 
< 0.1%
ecaeddeeecaadeeddcdacbabaacbadbbddedcbdbecebaecdde 1290
 
< 0.1%
deecaedececbddedbeceedaaddcdacbababadacebaecddebbd 1274
 
< 0.1%
bdbbabaaaeaaecbbeaacaeaceacbcacccededadbebeeddaccc 1273
 
< 0.1%
aedececdeecddeebaebecdedcbdbbdacababadacbdcdeedaad 1272
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 42307586
27.4%
C 31033680
20.1%
B 25398656
16.4%
E 22604214
14.6%
D 20899326
13.5%
c 2222624
 
1.4%
o 2222624
 
1.4%
e 2222624
 
1.4%
s 1111312
 
0.7%
d 1111312
 
0.7%
Other values (3) 3333936
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154467894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 42307586
27.4%
C 31033680
20.1%
B 25398656
16.4%
E 22604214
14.6%
D 20899326
13.5%
c 2222624
 
1.4%
o 2222624
 
1.4%
e 2222624
 
1.4%
s 1111312
 
0.7%
d 1111312
 
0.7%
Other values (3) 3333936
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154467894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 42307586
27.4%
C 31033680
20.1%
B 25398656
16.4%
E 22604214
14.6%
D 20899326
13.5%
c 2222624
 
1.4%
o 2222624
 
1.4%
e 2222624
 
1.4%
s 1111312
 
0.7%
d 1111312
 
0.7%
Other values (3) 3333936
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154467894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 42307586
27.4%
C 31033680
20.1%
B 25398656
16.4%
E 22604214
14.6%
D 20899326
13.5%
c 2222624
 
1.4%
o 2222624
 
1.4%
e 2222624
 
1.4%
s 1111312
 
0.7%
d 1111312
 
0.7%
Other values (3) 3333936
 
2.2%

TX_GABARITO_MT
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
Desconhecido
1241528 
EBDEEBDADDABCCBCCABBADDBDDAEAEABCBEDECADCECCB
680789 
EBDADDAEBEACBEDCECCBEABCADEBCCBCCDEBDDAABBADD
669321 
DCECACCBDECBEEABEABDDAADDABBBCCBCCDDAEBDADEEB
668909 
BCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC
668204 
Other values (4)
 
5204

Length

Max length45
Median length45
Mean length34.585437
Min length12

Characters and Unicode

Total characters136057551
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesconhecido
2nd rowDesconhecido
3rd rowBCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC
4th rowEBDADDAEBEACBEDCECCBEABCADEBCCBCCDEBDDAABBADD
5th rowDCECACCBDECBEEABEABDDAADDABBBCCBCCDDAEBDADEEB

Common Values

ValueCountFrequency (%)
Desconhecido 1241528
31.6%
EBDEEBDADDABCCBCCABBADDBDDAEAEABCBEDECADCECCB 680789
17.3%
EBDADDAEBEACBEDCECCBEABCADEBCCBCCDEBDDAABBADD 669321
17.0%
DCECACCBDECBEEABEABDDAADDABBBCCBCCDDAEBDADEEB 668909
17.0%
BCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC 668204
17.0%
CEBDEABDEDEBAABEECBABADDBEDCCCDDBEECCDBAADDAC 1313
 
< 0.1%
DDACDBAAECCDDBECCBEDCBADDECBAAABEEBBDEDDEACEB 1301
 
< 0.1%
BDEDAABEEBCCBADDCEBDEAECBECCBEDCDBAADDACDDBEA 1297
 
< 0.1%
BADDDEADDACBEDCECCCEBDDBEDBAACCAECBAABEEBBDED 1293
 
< 0.1%

Length

2025-04-15T16:06:01.674370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:01.913267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
desconhecido 1241528
31.6%
ebdeebdaddabccbccabbaddbddaeaeabcbedecadceccb 680789
17.3%
ebdaddaebeacbedceccbeabcadebccbccdebddaabbadd 669321
17.0%
dcecaccbdecbeeabeabddaaddabbbccbccddaebdadeeb 668909
17.0%
bccdeeabcbedceabbebdabddaddadecaaddccbebeabcc 668204
17.0%
cebdeabdedebaabeecbabaddbedcccddbeeccdbaaddac 1313
 
< 0.1%
ddacdbaaeccddbeccbedcbaddecbaaabeebbdeddeaceb 1301
 
< 0.1%
bdedaabeebccbaddcebdeaecbeccbedcdbaaddacddbea 1297
 
< 0.1%
badddeaddacbedcecccebddbedbaaccaecbaabeebbded 1293
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 28171002
20.7%
B 26919066
19.8%
C 24226639
17.8%
E 21544620
15.8%
A 21539416
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 28171002
20.7%
B 26919066
19.8%
C 24226639
17.8%
E 21544620
15.8%
A 21539416
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 28171002
20.7%
B 26919066
19.8%
C 24226639
17.8%
E 21544620
15.8%
A 21539416
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136057551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 28171002
20.7%
B 26919066
19.8%
C 24226639
17.8%
E 21544620
15.8%
A 21539416
15.8%
c 2483056
 
1.8%
o 2483056
 
1.8%
e 2483056
 
1.8%
s 1241528
 
0.9%
d 1241528
 
0.9%
Other values (3) 3724584
 
2.7%

TP_STATUS_REDACAO
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1.0
2704814 
-1.0
1111312 
4.0
 
55403
6.0
 
24466
3.0
 
22505
Other values (4)
 
15455

Length

Max length4
Median length3
Mean length3.2824923
Min length3

Characters and Unicode

Total characters12913177
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2704814
68.8%
-1.0 1111312
28.2%
4.0 55403
 
1.4%
6.0 24466
 
0.6%
3.0 22505
 
0.6%
8.0 9091
 
0.2%
9.0 2443
 
0.1%
2.0 2016
 
0.1%
7.0 1905
 
< 0.1%

Length

2025-04-15T16:06:02.201308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:02.434086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3816126
97.0%
4.0 55403
 
1.4%
6.0 24466
 
0.6%
3.0 22505
 
0.6%
8.0 9091
 
0.2%
9.0 2443
 
0.1%
2.0 2016
 
0.1%
7.0 1905
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 3933955
30.5%
0 3933955
30.5%
1 3816126
29.6%
- 1111312
 
8.6%
4 55403
 
0.4%
6 24466
 
0.2%
3 22505
 
0.2%
8 9091
 
0.1%
9 2443
 
< 0.1%
2 2016
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12913177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3933955
30.5%
0 3933955
30.5%
1 3816126
29.6%
- 1111312
 
8.6%
4 55403
 
0.4%
6 24466
 
0.2%
3 22505
 
0.2%
8 9091
 
0.1%
9 2443
 
< 0.1%
2 2016
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12913177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3933955
30.5%
0 3933955
30.5%
1 3816126
29.6%
- 1111312
 
8.6%
4 55403
 
0.4%
6 24466
 
0.2%
3 22505
 
0.2%
8 9091
 
0.1%
9 2443
 
< 0.1%
2 2016
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12913177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3933955
30.5%
0 3933955
30.5%
1 3816126
29.6%
- 1111312
 
8.6%
4 55403
 
0.4%
6 24466
 
0.2%
3 22505
 
0.2%
8 9091
 
0.1%
9 2443
 
< 0.1%
2 2016
 
< 0.1%

NU_NOTA_COMP1
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.927112
Minimum-1
Maximum200
Zeros118035
Zeros (%)3.0%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:06:02.714223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median120
Q3140
95-th percentile160
Maximum200
Range201
Interquartile range (IQR)141

Descriptive statistics

Standard deviation62.896672
Coefficient of variation (CV)0.72355644
Kurtosis-1.3886115
Mean86.927112
Median Absolute Deviation (MAD)40
Skewness-0.46512686
Sum3.4196735 × 108
Variance3955.9914
MonotonicityNot monotonic
2025-04-15T16:06:02.971098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1 1111312
28.2%
120 1049425
26.7%
160 561001
14.3%
140 446453
11.3%
100 364300
 
9.3%
80 188382
 
4.8%
0 118035
 
3.0%
180 60381
 
1.5%
60 24388
 
0.6%
40 5954
 
0.2%
Other values (2) 4324
 
0.1%
ValueCountFrequency (%)
-1 1111312
28.2%
0 118035
 
3.0%
20 285
 
< 0.1%
40 5954
 
0.2%
60 24388
 
0.6%
80 188382
 
4.8%
100 364300
 
9.3%
120 1049425
26.7%
140 446453
11.3%
160 561001
14.3%
ValueCountFrequency (%)
200 4039
 
0.1%
180 60381
 
1.5%
160 561001
14.3%
140 446453
11.3%
120 1049425
26.7%
100 364300
 
9.3%
80 188382
 
4.8%
60 24388
 
0.6%
40 5954
 
0.2%
20 285
 
< 0.1%

NU_NOTA_COMP2
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.662906
Minimum-1
Maximum200
Zeros117829
Zeros (%)3.0%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:06:03.193155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median120
Q3160
95-th percentile200
Maximum200
Range201
Interquartile range (IQR)161

Descriptive statistics

Standard deviation76.818422
Coefficient of variation (CV)0.77078248
Kurtosis-1.468097
Mean99.662906
Median Absolute Deviation (MAD)80
Skewness-0.21538915
Sum3.9206939 × 108
Variance5901.07
MonotonicityNot monotonic
2025-04-15T16:06:03.432087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1 1111312
28.2%
120 913657
23.2%
200 626079
15.9%
180 352112
 
9.0%
160 276916
 
7.0%
140 181742
 
4.6%
40 127173
 
3.2%
100 125418
 
3.2%
0 117829
 
3.0%
80 73186
 
1.9%
ValueCountFrequency (%)
-1 1111312
28.2%
0 117829
 
3.0%
40 127173
 
3.2%
60 28531
 
0.7%
80 73186
 
1.9%
100 125418
 
3.2%
120 913657
23.2%
140 181742
 
4.6%
160 276916
 
7.0%
180 352112
 
9.0%
ValueCountFrequency (%)
200 626079
15.9%
180 352112
 
9.0%
160 276916
 
7.0%
140 181742
 
4.6%
120 913657
23.2%
100 125418
 
3.2%
80 73186
 
1.9%
60 28531
 
0.7%
40 127173
 
3.2%
0 117829
 
3.0%

NU_NOTA_COMP3
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.748661
Minimum-1
Maximum200
Zeros118280
Zeros (%)3.0%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:06:03.665628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median100
Q3120
95-th percentile180
Maximum200
Range201
Interquartile range (IQR)121

Descriptive statistics

Standard deviation65.168192
Coefficient of variation (CV)0.76895837
Kurtosis-1.3565901
Mean84.748661
Median Absolute Deviation (MAD)60
Skewness-0.19579525
Sum3.3339742 × 108
Variance4246.8933
MonotonicityNot monotonic
2025-04-15T16:06:03.844675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-1 1111312
28.2%
120 959221
24.4%
160 346374
 
8.8%
100 344925
 
8.8%
140 326990
 
8.3%
80 237990
 
6.0%
180 198937
 
5.1%
40 130023
 
3.3%
0 118280
 
3.0%
200 100988
 
2.6%
Other values (3) 58915
 
1.5%
ValueCountFrequency (%)
-1 1111312
28.2%
0 118280
 
3.0%
10 1
 
< 0.1%
20 1749
 
< 0.1%
40 130023
 
3.3%
60 57165
 
1.5%
80 237990
 
6.0%
100 344925
 
8.8%
120 959221
24.4%
140 326990
 
8.3%
ValueCountFrequency (%)
200 100988
 
2.6%
180 198937
 
5.1%
160 346374
 
8.8%
140 326990
 
8.3%
120 959221
24.4%
100 344925
 
8.8%
80 237990
 
6.0%
60 57165
 
1.5%
40 130023
 
3.3%
20 1749
 
< 0.1%

NU_NOTA_COMP4
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.847866
Minimum-1
Maximum200
Zeros118340
Zeros (%)3.0%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:06:04.081745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median120
Q3140
95-th percentile200
Maximum200
Range201
Interquartile range (IQR)141

Descriptive statistics

Standard deviation69.58746
Coefficient of variation (CV)0.74947829
Kurtosis-1.3256561
Mean92.847866
Median Absolute Deviation (MAD)40
Skewness-0.26736491
Sum3.6525933 × 108
Variance4842.4146
MonotonicityNot monotonic
2025-04-15T16:06:04.323467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1 1111312
28.2%
120 934121
23.7%
160 380163
 
9.7%
140 332737
 
8.5%
100 299878
 
7.6%
180 263147
 
6.7%
200 259659
 
6.6%
80 187421
 
4.8%
0 118340
 
3.0%
60 36289
 
0.9%
Other values (2) 10888
 
0.3%
ValueCountFrequency (%)
-1 1111312
28.2%
0 118340
 
3.0%
20 1287
 
< 0.1%
40 9601
 
0.2%
60 36289
 
0.9%
80 187421
 
4.8%
100 299878
 
7.6%
120 934121
23.7%
140 332737
 
8.5%
160 380163
 
9.7%
ValueCountFrequency (%)
200 259659
 
6.6%
180 263147
 
6.7%
160 380163
9.7%
140 332737
 
8.5%
120 934121
23.7%
100 299878
 
7.6%
80 187421
 
4.8%
60 36289
 
0.9%
40 9601
 
0.2%
20 1287
 
< 0.1%

NU_NOTA_COMP5
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.676152
Minimum-1
Maximum200
Zeros290015
Zeros (%)7.4%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:06:04.570061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median80
Q3140
95-th percentile200
Maximum200
Range201
Interquartile range (IQR)141

Descriptive statistics

Standard deviation71.832487
Coefficient of variation (CV)0.92476886
Kurtosis-1.379304
Mean77.676152
Median Absolute Deviation (MAD)80
Skewness0.25969289
Sum3.0557449 × 108
Variance5159.9062
MonotonicityNot monotonic
2025-04-15T16:06:04.798462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1 1111312
28.2%
120 408272
 
10.4%
200 320909
 
8.2%
80 301546
 
7.7%
160 299417
 
7.6%
0 290015
 
7.4%
100 272521
 
6.9%
140 249908
 
6.4%
180 222022
 
5.6%
40 215940
 
5.5%
Other values (2) 242093
 
6.2%
ValueCountFrequency (%)
-1 1111312
28.2%
0 290015
 
7.4%
20 97135
 
2.5%
40 215940
 
5.5%
60 144958
 
3.7%
80 301546
 
7.7%
100 272521
 
6.9%
120 408272
 
10.4%
140 249908
 
6.4%
160 299417
 
7.6%
ValueCountFrequency (%)
200 320909
8.2%
180 222022
5.6%
160 299417
7.6%
140 249908
6.4%
120 408272
10.4%
100 272521
6.9%
80 301546
7.7%
60 144958
 
3.7%
40 215940
5.5%
20 97135
 
2.5%

NU_NOTA_REDACAO
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.99267
Minimum-1
Maximum1000
Zeros117829
Zeros (%)3.0%
Negative1111312
Negative (%)28.2%
Memory size30.0 MiB
2025-04-15T16:06:05.165523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median540
Q3700
95-th percentile920
Maximum1000
Range1001
Interquartile range (IQR)701

Descriptive statistics

Standard deviation332.65974
Coefficient of variation (CV)0.75093733
Kurtosis-1.3832077
Mean442.99267
Median Absolute Deviation (MAD)240
Skewness-0.24491942
Sum1.7427132 × 109
Variance110662.5
MonotonicityNot monotonic
2025-04-15T16:06:05.516359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1111312
28.2%
560 152243
 
3.9%
600 151617
 
3.9%
580 125191
 
3.2%
640 124006
 
3.2%
520 119386
 
3.0%
0 117829
 
3.0%
540 105765
 
2.7%
620 105685
 
2.7%
680 103162
 
2.6%
Other values (41) 1717759
43.7%
ValueCountFrequency (%)
-1 1111312
28.2%
0 117829
 
3.0%
40 121
 
< 0.1%
60 93
 
< 0.1%
80 143
 
< 0.1%
100 71
 
< 0.1%
120 219
 
< 0.1%
140 185
 
< 0.1%
160 643
 
< 0.1%
180 806
 
< 0.1%
ValueCountFrequency (%)
1000 60
 
< 0.1%
980 12836
 
0.3%
960 43690
1.1%
940 68706
1.7%
920 89062
2.3%
900 74308
1.9%
880 88638
2.3%
860 64082
1.6%
840 84224
2.1%
820 62392
1.6%

Q001
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
E
1114164 
B
700175 
C
513213 
D
438139 
H
403526 
Other values (3)
764738 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowF
3rd rowH
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
E 1114164
28.3%
B 700175
17.8%
C 513213
13.0%
D 438139
 
11.1%
H 403526
 
10.3%
F 333981
 
8.5%
G 256045
 
6.5%
A 174712
 
4.4%

Length

2025-04-15T16:06:05.801248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:06.058942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
e 1114164
28.3%
b 700175
17.8%
c 513213
13.0%
d 438139
 
11.1%
h 403526
 
10.3%
f 333981
 
8.5%
g 256045
 
6.5%
a 174712
 
4.4%

Most occurring characters

ValueCountFrequency (%)
E 1114164
28.3%
B 700175
17.8%
C 513213
13.0%
D 438139
 
11.1%
H 403526
 
10.3%
F 333981
 
8.5%
G 256045
 
6.5%
A 174712
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1114164
28.3%
B 700175
17.8%
C 513213
13.0%
D 438139
 
11.1%
H 403526
 
10.3%
F 333981
 
8.5%
G 256045
 
6.5%
A 174712
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1114164
28.3%
B 700175
17.8%
C 513213
13.0%
D 438139
 
11.1%
H 403526
 
10.3%
F 333981
 
8.5%
G 256045
 
6.5%
A 174712
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1114164
28.3%
B 700175
17.8%
C 513213
13.0%
D 438139
 
11.1%
H 403526
 
10.3%
F 333981
 
8.5%
G 256045
 
6.5%
A 174712
 
4.4%

Q002
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
E
1377605 
B
505525 
D
473854 
F
455268 
G
441893 
Other values (3)
679810 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowE
3rd rowE
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
E 1377605
35.0%
B 505525
 
12.9%
D 473854
 
12.0%
F 455268
 
11.6%
G 441893
 
11.2%
C 437022
 
11.1%
H 131957
 
3.4%
A 110831
 
2.8%

Length

2025-04-15T16:06:06.309512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:06.465793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
e 1377605
35.0%
b 505525
 
12.9%
d 473854
 
12.0%
f 455268
 
11.6%
g 441893
 
11.2%
c 437022
 
11.1%
h 131957
 
3.4%
a 110831
 
2.8%

Most occurring characters

ValueCountFrequency (%)
E 1377605
35.0%
B 505525
 
12.9%
D 473854
 
12.0%
F 455268
 
11.6%
G 441893
 
11.2%
C 437022
 
11.1%
H 131957
 
3.4%
A 110831
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1377605
35.0%
B 505525
 
12.9%
D 473854
 
12.0%
F 455268
 
11.6%
G 441893
 
11.2%
C 437022
 
11.1%
H 131957
 
3.4%
A 110831
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1377605
35.0%
B 505525
 
12.9%
D 473854
 
12.0%
F 455268
 
11.6%
G 441893
 
11.2%
C 437022
 
11.1%
H 131957
 
3.4%
A 110831
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1377605
35.0%
B 505525
 
12.9%
D 473854
 
12.0%
F 455268
 
11.6%
G 441893
 
11.2%
C 437022
 
11.1%
H 131957
 
3.4%
A 110831
 
2.8%

Q003
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
C
911756 
B
790173 
A
750778 
D
706775 
F
506281 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowC
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
C 911756
23.2%
B 790173
20.1%
A 750778
19.1%
D 706775
18.0%
F 506281
12.9%
E 268192
 
6.8%

Length

2025-04-15T16:06:06.744038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:06.915462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
c 911756
23.2%
b 790173
20.1%
a 750778
19.1%
d 706775
18.0%
f 506281
12.9%
e 268192
 
6.8%

Most occurring characters

ValueCountFrequency (%)
C 911756
23.2%
B 790173
20.1%
A 750778
19.1%
D 706775
18.0%
F 506281
12.9%
E 268192
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 911756
23.2%
B 790173
20.1%
A 750778
19.1%
D 706775
18.0%
F 506281
12.9%
E 268192
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 911756
23.2%
B 790173
20.1%
A 750778
19.1%
D 706775
18.0%
F 506281
12.9%
E 268192
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 911756
23.2%
B 790173
20.1%
A 750778
19.1%
D 706775
18.0%
F 506281
12.9%
E 268192
 
6.8%

Q004
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
1632671 
D
892420 
A
608127 
F
352995 
C
247158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowB
3rd rowF
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 1632671
41.5%
D 892420
22.7%
A 608127
 
15.5%
F 352995
 
9.0%
C 247158
 
6.3%
E 200584
 
5.1%

Length

2025-04-15T16:06:07.112520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:07.329764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 1632671
41.5%
d 892420
22.7%
a 608127
 
15.5%
f 352995
 
9.0%
c 247158
 
6.3%
e 200584
 
5.1%

Most occurring characters

ValueCountFrequency (%)
B 1632671
41.5%
D 892420
22.7%
A 608127
 
15.5%
F 352995
 
9.0%
C 247158
 
6.3%
E 200584
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1632671
41.5%
D 892420
22.7%
A 608127
 
15.5%
F 352995
 
9.0%
C 247158
 
6.3%
E 200584
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1632671
41.5%
D 892420
22.7%
A 608127
 
15.5%
F 352995
 
9.0%
C 247158
 
6.3%
E 200584
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1632671
41.5%
D 892420
22.7%
A 608127
 
15.5%
F 352995
 
9.0%
C 247158
 
6.3%
E 200584
 
5.1%

Q005
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
4
1247259 
3
1086674 
2
567635 
5
566325 
6
194132 
Other values (15)
271930 

Length

Max length2
Median length1
Mean length1.0032654
Min length1

Characters and Unicode

Total characters3946801
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
4 1247259
31.7%
3 1086674
27.6%
2 567635
14.4%
5 566325
14.4%
6 194132
 
4.9%
1 146875
 
3.7%
7 71049
 
1.8%
8 29968
 
0.8%
9 11192
 
0.3%
10 6826
 
0.2%
Other values (10) 6020
 
0.2%

Length

2025-04-15T16:06:07.592871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 1247259
31.7%
3 1086674
27.6%
2 567635
14.4%
5 566325
14.4%
6 194132
 
4.9%
1 146875
 
3.7%
7 71049
 
1.8%
8 29968
 
0.8%
9 11192
 
0.3%
10 6826
 
0.2%
Other values (10) 6020
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 1247623
31.6%
3 1087321
27.5%
2 569637
14.4%
5 566673
14.4%
6 194258
 
4.9%
1 161497
 
4.1%
7 71158
 
1.8%
8 30054
 
0.8%
9 11249
 
0.3%
0 7331
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3946801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 1247623
31.6%
3 1087321
27.5%
2 569637
14.4%
5 566673
14.4%
6 194258
 
4.9%
1 161497
 
4.1%
7 71158
 
1.8%
8 30054
 
0.8%
9 11249
 
0.3%
0 7331
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3946801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 1247623
31.6%
3 1087321
27.5%
2 569637
14.4%
5 566673
14.4%
6 194258
 
4.9%
1 161497
 
4.1%
7 71158
 
1.8%
8 30054
 
0.8%
9 11249
 
0.3%
0 7331
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3946801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 1247623
31.6%
3 1087321
27.5%
2 569637
14.4%
5 566673
14.4%
6 194258
 
4.9%
1 161497
 
4.1%
7 71158
 
1.8%
8 30054
 
0.8%
9 11249
 
0.3%
0 7331
 
0.2%

Q006
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
1245271 
C
650942 
D
437366 
E
293994 
A
268053 
Other values (12)
1038329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowH
3rd rowC
4th rowC
5th rowB

Common Values

ValueCountFrequency (%)
B 1245271
31.7%
C 650942
16.5%
D 437366
 
11.1%
E 293994
 
7.5%
A 268053
 
6.8%
G 261327
 
6.6%
F 171344
 
4.4%
H 139279
 
3.5%
I 85970
 
2.2%
J 75179
 
1.9%
Other values (7) 305230
 
7.8%

Length

2025-04-15T16:06:07.795651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b 1245271
31.7%
c 650942
16.5%
d 437366
 
11.1%
e 293994
 
7.5%
a 268053
 
6.8%
g 261327
 
6.6%
f 171344
 
4.4%
h 139279
 
3.5%
i 85970
 
2.2%
j 75179
 
1.9%
Other values (7) 305230
 
7.8%

Most occurring characters

ValueCountFrequency (%)
B 1245271
31.7%
C 650942
16.5%
D 437366
 
11.1%
E 293994
 
7.5%
A 268053
 
6.8%
G 261327
 
6.6%
F 171344
 
4.4%
H 139279
 
3.5%
I 85970
 
2.2%
J 75179
 
1.9%
Other values (7) 305230
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1245271
31.7%
C 650942
16.5%
D 437366
 
11.1%
E 293994
 
7.5%
A 268053
 
6.8%
G 261327
 
6.6%
F 171344
 
4.4%
H 139279
 
3.5%
I 85970
 
2.2%
J 75179
 
1.9%
Other values (7) 305230
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1245271
31.7%
C 650942
16.5%
D 437366
 
11.1%
E 293994
 
7.5%
A 268053
 
6.8%
G 261327
 
6.6%
F 171344
 
4.4%
H 139279
 
3.5%
I 85970
 
2.2%
J 75179
 
1.9%
Other values (7) 305230
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1245271
31.7%
C 650942
16.5%
D 437366
 
11.1%
E 293994
 
7.5%
A 268053
 
6.8%
G 261327
 
6.6%
F 171344
 
4.4%
H 139279
 
3.5%
I 85970
 
2.2%
J 75179
 
1.9%
Other values (7) 305230
 
7.8%

Q007
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3611179 
B
 
178524
D
 
104370
C
 
39882

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3611179
91.8%
B 178524
 
4.5%
D 104370
 
2.7%
C 39882
 
1.0%

Length

2025-04-15T16:06:07.994689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:08.175695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3611179
91.8%
b 178524
 
4.5%
d 104370
 
2.7%
c 39882
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A 3611179
91.8%
B 178524
 
4.5%
D 104370
 
2.7%
C 39882
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3611179
91.8%
B 178524
 
4.5%
D 104370
 
2.7%
C 39882
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3611179
91.8%
B 178524
 
4.5%
D 104370
 
2.7%
C 39882
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3611179
91.8%
B 178524
 
4.5%
D 104370
 
2.7%
C 39882
 
1.0%

Q008
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
2647503 
C
855720 
D
 
253087
E
 
139767
A
 
37878

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 2647503
67.3%
C 855720
 
21.8%
D 253087
 
6.4%
E 139767
 
3.6%
A 37878
 
1.0%

Length

2025-04-15T16:06:08.348006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:08.543849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 2647503
67.3%
c 855720
 
21.8%
d 253087
 
6.4%
e 139767
 
3.6%
a 37878
 
1.0%

Most occurring characters

ValueCountFrequency (%)
B 2647503
67.3%
C 855720
 
21.8%
D 253087
 
6.4%
E 139767
 
3.6%
A 37878
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 2647503
67.3%
C 855720
 
21.8%
D 253087
 
6.4%
E 139767
 
3.6%
A 37878
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 2647503
67.3%
C 855720
 
21.8%
D 253087
 
6.4%
E 139767
 
3.6%
A 37878
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 2647503
67.3%
C 855720
 
21.8%
D 253087
 
6.4%
E 139767
 
3.6%
A 37878
 
1.0%

Q009
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
C
1968685 
D
1167141 
B
560699 
E
202961 
A
 
34469

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowC
3rd rowD
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
C 1968685
50.0%
D 1167141
29.7%
B 560699
 
14.3%
E 202961
 
5.2%
A 34469
 
0.9%

Length

2025-04-15T16:06:08.762495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:08.927407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
c 1968685
50.0%
d 1167141
29.7%
b 560699
 
14.3%
e 202961
 
5.2%
a 34469
 
0.9%

Most occurring characters

ValueCountFrequency (%)
C 1968685
50.0%
D 1167141
29.7%
B 560699
 
14.3%
E 202961
 
5.2%
A 34469
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1968685
50.0%
D 1167141
29.7%
B 560699
 
14.3%
E 202961
 
5.2%
A 34469
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1968685
50.0%
D 1167141
29.7%
B 560699
 
14.3%
E 202961
 
5.2%
A 34469
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1968685
50.0%
D 1167141
29.7%
B 560699
 
14.3%
E 202961
 
5.2%
A 34469
 
0.9%

Q010
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
2167356 
B
1392596 
C
323334 
D
 
41185
E
 
9484

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 2167356
55.1%
B 1392596
35.4%
C 323334
 
8.2%
D 41185
 
1.0%
E 9484
 
0.2%

Length

2025-04-15T16:06:09.193267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:09.402897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 2167356
55.1%
b 1392596
35.4%
c 323334
 
8.2%
d 41185
 
1.0%
e 9484
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 2167356
55.1%
B 1392596
35.4%
C 323334
 
8.2%
D 41185
 
1.0%
E 9484
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2167356
55.1%
B 1392596
35.4%
C 323334
 
8.2%
D 41185
 
1.0%
E 9484
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2167356
55.1%
B 1392596
35.4%
C 323334
 
8.2%
D 41185
 
1.0%
E 9484
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2167356
55.1%
B 1392596
35.4%
C 323334
 
8.2%
D 41185
 
1.0%
E 9484
 
0.2%

Q011
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
2983690 
B
842923 
C
 
95329
D
 
10022
E
 
1991

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 2983690
75.8%
B 842923
 
21.4%
C 95329
 
2.4%
D 10022
 
0.3%
E 1991
 
0.1%

Length

2025-04-15T16:06:09.667580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:09.887465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 2983690
75.8%
b 842923
 
21.4%
c 95329
 
2.4%
d 10022
 
0.3%
e 1991
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 2983690
75.8%
B 842923
 
21.4%
C 95329
 
2.4%
D 10022
 
0.3%
E 1991
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2983690
75.8%
B 842923
 
21.4%
C 95329
 
2.4%
D 10022
 
0.3%
E 1991
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2983690
75.8%
B 842923
 
21.4%
C 95329
 
2.4%
D 10022
 
0.3%
E 1991
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2983690
75.8%
B 842923
 
21.4%
C 95329
 
2.4%
D 10022
 
0.3%
E 1991
 
0.1%

Q012
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
3653942 
C
 
186456
A
 
75430
D
 
14986
E
 
3141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 3653942
92.9%
C 186456
 
4.7%
A 75430
 
1.9%
D 14986
 
0.4%
E 3141
 
0.1%

Length

2025-04-15T16:06:10.095426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:10.275089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 3653942
92.9%
c 186456
 
4.7%
a 75430
 
1.9%
d 14986
 
0.4%
e 3141
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 3653942
92.9%
C 186456
 
4.7%
A 75430
 
1.9%
D 14986
 
0.4%
E 3141
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 3653942
92.9%
C 186456
 
4.7%
A 75430
 
1.9%
D 14986
 
0.4%
E 3141
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 3653942
92.9%
C 186456
 
4.7%
A 75430
 
1.9%
D 14986
 
0.4%
E 3141
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 3653942
92.9%
C 186456
 
4.7%
A 75430
 
1.9%
D 14986
 
0.4%
E 3141
 
0.1%

Q013
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
2217219 
B
1580612 
C
 
117473
D
 
15112
E
 
3539

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 2217219
56.4%
B 1580612
40.2%
C 117473
 
3.0%
D 15112
 
0.4%
E 3539
 
0.1%

Length

2025-04-15T16:06:10.425927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:10.706106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 2217219
56.4%
b 1580612
40.2%
c 117473
 
3.0%
d 15112
 
0.4%
e 3539
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 2217219
56.4%
B 1580612
40.2%
C 117473
 
3.0%
D 15112
 
0.4%
E 3539
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2217219
56.4%
B 1580612
40.2%
C 117473
 
3.0%
D 15112
 
0.4%
E 3539
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2217219
56.4%
B 1580612
40.2%
C 117473
 
3.0%
D 15112
 
0.4%
E 3539
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2217219
56.4%
B 1580612
40.2%
C 117473
 
3.0%
D 15112
 
0.4%
E 3539
 
0.1%

Q014
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
2368207 
A
1517308 
C
 
46133
D
 
1863
E
 
444

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 2368207
60.2%
A 1517308
38.6%
C 46133
 
1.2%
D 1863
 
< 0.1%
E 444
 
< 0.1%

Length

2025-04-15T16:06:10.936064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:11.088535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 2368207
60.2%
a 1517308
38.6%
c 46133
 
1.2%
d 1863
 
< 0.1%
e 444
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 2368207
60.2%
A 1517308
38.6%
C 46133
 
1.2%
D 1863
 
< 0.1%
E 444
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 2368207
60.2%
A 1517308
38.6%
C 46133
 
1.2%
D 1863
 
< 0.1%
E 444
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 2368207
60.2%
A 1517308
38.6%
C 46133
 
1.2%
D 1863
 
< 0.1%
E 444
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 2368207
60.2%
A 1517308
38.6%
C 46133
 
1.2%
D 1863
 
< 0.1%
E 444
 
< 0.1%

Q015
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3438821 
B
488250 
C
 
6110
D
 
501
E
 
273

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3438821
87.4%
B 488250
 
12.4%
C 6110
 
0.2%
D 501
 
< 0.1%
E 273
 
< 0.1%

Length

2025-04-15T16:06:11.319360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:11.549716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3438821
87.4%
b 488250
 
12.4%
c 6110
 
0.2%
d 501
 
< 0.1%
e 273
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 3438821
87.4%
B 488250
 
12.4%
C 6110
 
0.2%
D 501
 
< 0.1%
E 273
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3438821
87.4%
B 488250
 
12.4%
C 6110
 
0.2%
D 501
 
< 0.1%
E 273
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3438821
87.4%
B 488250
 
12.4%
C 6110
 
0.2%
D 501
 
< 0.1%
E 273
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3438821
87.4%
B 488250
 
12.4%
C 6110
 
0.2%
D 501
 
< 0.1%
E 273
 
< 0.1%

Q016
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
2064853 
B
1838403 
C
 
28698
D
 
1515
E
 
486

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 2064853
52.5%
B 1838403
46.7%
C 28698
 
0.7%
D 1515
 
< 0.1%
E 486
 
< 0.1%

Length

2025-04-15T16:06:11.811442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:11.987375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 2064853
52.5%
b 1838403
46.7%
c 28698
 
0.7%
d 1515
 
< 0.1%
e 486
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 2064853
52.5%
B 1838403
46.7%
C 28698
 
0.7%
D 1515
 
< 0.1%
E 486
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2064853
52.5%
B 1838403
46.7%
C 28698
 
0.7%
D 1515
 
< 0.1%
E 486
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2064853
52.5%
B 1838403
46.7%
C 28698
 
0.7%
D 1515
 
< 0.1%
E 486
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2064853
52.5%
B 1838403
46.7%
C 28698
 
0.7%
D 1515
 
< 0.1%
E 486
 
< 0.1%

Q017
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3803308 
B
 
127927
C
 
2180
D
 
280
E
 
260

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3803308
96.7%
B 127927
 
3.3%
C 2180
 
0.1%
D 280
 
< 0.1%
E 260
 
< 0.1%

Length

2025-04-15T16:06:12.208488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:12.399047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3803308
96.7%
b 127927
 
3.3%
c 2180
 
0.1%
d 280
 
< 0.1%
e 260
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 3803308
96.7%
B 127927
 
3.3%
C 2180
 
0.1%
D 280
 
< 0.1%
E 260
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3803308
96.7%
B 127927
 
3.3%
C 2180
 
0.1%
D 280
 
< 0.1%
E 260
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3803308
96.7%
B 127927
 
3.3%
C 2180
 
0.1%
D 280
 
< 0.1%
E 260
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3803308
96.7%
B 127927
 
3.3%
C 2180
 
0.1%
D 280
 
< 0.1%
E 260
 
< 0.1%

Q018
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3032209 
B
901746 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3032209
77.1%
B 901746
 
22.9%

Length

2025-04-15T16:06:12.618862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:12.779570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3032209
77.1%
b 901746
 
22.9%

Most occurring characters

ValueCountFrequency (%)
A 3032209
77.1%
B 901746
 
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3032209
77.1%
B 901746
 
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3032209
77.1%
B 901746
 
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3032209
77.1%
B 901746
 
22.9%

Q019
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
2607933 
C
661090 
A
305208 
D
 
241516
E
 
118208

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 2607933
66.3%
C 661090
 
16.8%
A 305208
 
7.8%
D 241516
 
6.1%
E 118208
 
3.0%

Length

2025-04-15T16:06:12.988133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:13.226524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 2607933
66.3%
c 661090
 
16.8%
a 305208
 
7.8%
d 241516
 
6.1%
e 118208
 
3.0%

Most occurring characters

ValueCountFrequency (%)
B 2607933
66.3%
C 661090
 
16.8%
A 305208
 
7.8%
D 241516
 
6.1%
E 118208
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 2607933
66.3%
C 661090
 
16.8%
A 305208
 
7.8%
D 241516
 
6.1%
E 118208
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 2607933
66.3%
C 661090
 
16.8%
A 305208
 
7.8%
D 241516
 
6.1%
E 118208
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 2607933
66.3%
C 661090
 
16.8%
A 305208
 
7.8%
D 241516
 
6.1%
E 118208
 
3.0%

Q020
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3430585 
B
503370 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3430585
87.2%
B 503370
 
12.8%

Length

2025-04-15T16:06:13.471156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:13.660622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3430585
87.2%
b 503370
 
12.8%

Most occurring characters

ValueCountFrequency (%)
A 3430585
87.2%
B 503370
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3430585
87.2%
B 503370
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3430585
87.2%
B 503370
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3430585
87.2%
B 503370
 
12.8%

Q021
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3100005 
B
833950 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3100005
78.8%
B 833950
 
21.2%

Length

2025-04-15T16:06:13.841834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:14.006829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3100005
78.8%
b 833950
 
21.2%

Most occurring characters

ValueCountFrequency (%)
A 3100005
78.8%
B 833950
 
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3100005
78.8%
B 833950
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3100005
78.8%
B 833950
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3100005
78.8%
B 833950
 
21.2%

Q022
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
D
1138368 
C
1087753 
E
841389 
B
764861 
A
 
101584

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowA
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
D 1138368
28.9%
C 1087753
27.7%
E 841389
21.4%
B 764861
19.4%
A 101584
 
2.6%

Length

2025-04-15T16:06:14.196246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:14.426830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
d 1138368
28.9%
c 1087753
27.7%
e 841389
21.4%
b 764861
19.4%
a 101584
 
2.6%

Most occurring characters

ValueCountFrequency (%)
D 1138368
28.9%
C 1087753
27.7%
E 841389
21.4%
B 764861
19.4%
A 101584
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1138368
28.9%
C 1087753
27.7%
E 841389
21.4%
B 764861
19.4%
A 101584
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1138368
28.9%
C 1087753
27.7%
E 841389
21.4%
B 764861
19.4%
A 101584
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1138368
28.9%
C 1087753
27.7%
E 841389
21.4%
B 764861
19.4%
A 101584
 
2.6%

Q023
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
3579617 
B
 
354338

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 3579617
91.0%
B 354338
 
9.0%

Length

2025-04-15T16:06:14.669836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:14.873311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 3579617
91.0%
b 354338
 
9.0%

Most occurring characters

ValueCountFrequency (%)
A 3579617
91.0%
B 354338
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3579617
91.0%
B 354338
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3579617
91.0%
B 354338
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3579617
91.0%
B 354338
 
9.0%

Q024
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
A
2041637 
B
1362634 
C
341120 
D
 
126296
E
 
62268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowD
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 2041637
51.9%
B 1362634
34.6%
C 341120
 
8.7%
D 126296
 
3.2%
E 62268
 
1.6%

Length

2025-04-15T16:06:15.083951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:15.333247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 2041637
51.9%
b 1362634
34.6%
c 341120
 
8.7%
d 126296
 
3.2%
e 62268
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A 2041637
51.9%
B 1362634
34.6%
C 341120
 
8.7%
D 126296
 
3.2%
E 62268
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2041637
51.9%
B 1362634
34.6%
C 341120
 
8.7%
D 126296
 
3.2%
E 62268
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2041637
51.9%
B 1362634
34.6%
C 341120
 
8.7%
D 126296
 
3.2%
E 62268
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2041637
51.9%
B 1362634
34.6%
C 341120
 
8.7%
D 126296
 
3.2%
E 62268
 
1.6%

Q025
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
B
3558451 
A
375504 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 3558451
90.5%
A 375504
 
9.5%

Length

2025-04-15T16:06:15.494769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:15.642132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 3558451
90.5%
a 375504
 
9.5%

Most occurring characters

ValueCountFrequency (%)
B 3558451
90.5%
A 375504
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 3558451
90.5%
A 375504
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 3558451
90.5%
A 375504
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 3558451
90.5%
A 375504
 
9.5%

TP_PRESENCA_GERAL
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
2678264 
0
1097149 
2
 
144379
3
 
14163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2678264
68.1%
0 1097149
27.9%
2 144379
 
3.7%
3 14163
 
0.4%

Length

2025-04-15T16:06:15.799652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:15.948177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2678264
68.1%
0 1097149
27.9%
2 144379
 
3.7%
3 14163
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 2678264
68.1%
0 1097149
27.9%
2 144379
 
3.7%
3 14163
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2678264
68.1%
0 1097149
27.9%
2 144379
 
3.7%
3 14163
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2678264
68.1%
0 1097149
27.9%
2 144379
 
3.7%
3 14163
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2678264
68.1%
0 1097149
27.9%
2 144379
 
3.7%
3 14163
 
0.4%

TP_PRESENCA_REDACAO
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
2822643 
0
1111312 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2822643
71.8%
0 1111312
 
28.2%

Length

2025-04-15T16:06:16.090451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:16.296349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2822643
71.8%
0 1111312
 
28.2%

Most occurring characters

ValueCountFrequency (%)
1 2822643
71.8%
0 1111312
 
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1111312
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1111312
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2822643
71.8%
0 1111312
 
28.2%

NU_DESEMPENHO
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2
1788098 
3
1421511 
1
724346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1788098
45.5%
3 1421511
36.1%
1 724346
18.4%

Length

2025-04-15T16:06:16.504874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:16.646719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1788098
45.5%
3 1421511
36.1%
1 724346
18.4%

Most occurring characters

ValueCountFrequency (%)
2 1788098
45.5%
3 1421511
36.1%
1 724346
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1788098
45.5%
3 1421511
36.1%
1 724346
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1788098
45.5%
3 1421511
36.1%
1 724346
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1788098
45.5%
3 1421511
36.1%
1 724346
18.4%

NU_MEDIA_GERAL
Real number (ℝ)

High correlation 

Distinct3349
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.74762
Minimum-1
Maximum862.5
Zeros20
Zeros (%)< 0.1%
Negative1098447
Negative (%)27.9%
Memory size30.0 MiB
2025-04-15T16:06:16.869674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median480
Q3572.5
95-th percentile681.5
Maximum862.5
Range863.5
Interquartile range (IQR)573.5

Descriptive statistics

Standard deviation254.76924
Coefficient of variation (CV)0.67266229
Kurtosis-1.2419913
Mean378.74762
Median Absolute Deviation (MAD)127.25
Skewness-0.57783318
Sum1.4899761 × 109
Variance64907.368
MonotonicityNot monotonic
2025-04-15T16:06:17.068883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 1097149
 
27.9%
535 6166
 
0.2%
534 6085
 
0.2%
523 6061
 
0.2%
533 6054
 
0.2%
525 6049
 
0.2%
530 6037
 
0.2%
520 6030
 
0.2%
536 6017
 
0.2%
517 5999
 
0.2%
Other values (3339) 2782308
70.7%
ValueCountFrequency (%)
-1 1097149
27.9%
-0.6000976562 316
 
< 0.1%
-0.3999023438 982
 
< 0.1%
0 20
 
< 0.1%
35.59375 1
 
< 0.1%
39.59375 2
 
< 0.1%
43.59375 1
 
< 0.1%
44 1
 
< 0.1%
47.59375 5
 
< 0.1%
51.59375 2
 
< 0.1%
ValueCountFrequency (%)
862.5 1
 
< 0.1%
850.5 1
 
< 0.1%
848 2
< 0.1%
847.5 2
< 0.1%
847 1
 
< 0.1%
844.5 3
< 0.1%
844 1
 
< 0.1%
843.5 2
< 0.1%
843 1
 
< 0.1%
842 3
< 0.1%

NU_INFRAESTRUTURA
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
1442574 
2
1349116 
3
1142265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3933955
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 1442574
36.7%
2 1349116
34.3%
3 1142265
29.0%

Length

2025-04-15T16:06:17.256083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-15T16:06:17.411121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1442574
36.7%
2 1349116
34.3%
3 1142265
29.0%

Most occurring characters

ValueCountFrequency (%)
1 1442574
36.7%
2 1349116
34.3%
3 1142265
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1442574
36.7%
2 1349116
34.3%
3 1142265
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1442574
36.7%
2 1349116
34.3%
3 1142265
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3933955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1442574
36.7%
2 1349116
34.3%
3 1142265
29.0%

Interactions

2025-04-15T16:02:54.799304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:48.130100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:00.435962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:12.287750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:25.541712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:38.323615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:51.603471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:04.187792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:16.957258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:29.681935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:42.222394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:55.910957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:49.210725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:01.449215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:13.470471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:26.776376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:39.313909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:52.848594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:05.333816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:18.016114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:30.810048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:43.232245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:57.129217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:50.331802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:02.492502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:14.476053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:27.934771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:40.465945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:53.999173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:06.513877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:19.121599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:31.797597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:44.431383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:58.286627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:51.227065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:03.445417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:15.581513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:29.031435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:41.688399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:55.038758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:07.542183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:20.106599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:33.045215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:45.489775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:59.330970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:52.326042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:04.522445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:16.645562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:30.223624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:42.747469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:56.285347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:08.694876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:21.230970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:34.242628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:46.618433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:03:00.494928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:53.570572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:05.582223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:17.840273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:31.426584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:44.077044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:57.271089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:09.910631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:22.522270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:35.429696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:47.927603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:03:01.587261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:54.745447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:06.774935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:19.026966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:32.680770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:45.441462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:58.452874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:11.016022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:23.840202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:36.640686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:49.097873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:03:02.667934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:55.785487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:07.934265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:20.500222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:33.751962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:46.704237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:59.754207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:12.246518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:24.989067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:37.835358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:50.187363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:03:03.758520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:56.803192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:08.987883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:21.786208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:34.837061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:47.867616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:00.873430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:13.465290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:26.138906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:38.897449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:51.455438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:03:04.809013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:57.995459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:10.121018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:23.015082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:35.885670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:49.210727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:01.972741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:14.668993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:27.240063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:40.123260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:52.470588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:03:05.819587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:00:59.180132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:11.165565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:24.117013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:37.071837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:01:50.430522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:03.093814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:15.787948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:28.416567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:41.102559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-15T16:02:53.522626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-15T16:06:17.799192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
NU_DESEMPENHONU_INFRAESTRUTURANU_MEDIA_GERALNU_NOTA_CHNU_NOTA_CNNU_NOTA_COMP1NU_NOTA_COMP2NU_NOTA_COMP3NU_NOTA_COMP4NU_NOTA_COMP5NU_NOTA_LCNU_NOTA_MTNU_NOTA_REDACAOQ001Q002Q003Q004Q005Q006Q007Q008Q009Q010Q011Q012Q013Q014Q015Q016Q017Q018Q019Q020Q021Q022Q023Q024Q025SG_UF_ESCSG_UF_PROVATP_ANO_CONCLUIUTP_COR_RACATP_DEPENDENCIA_ADM_ESCTP_ENSINOTP_ESTADO_CIVILTP_FAIXA_ETARIATP_LINGUATP_LOCALIZACAO_ESCTP_NACIONALIDADETP_PRESENCA_CHTP_PRESENCA_CNTP_PRESENCA_GERALTP_PRESENCA_LCTP_PRESENCA_MTTP_PRESENCA_REDACAOTP_SEXOTP_STATUS_REDACAOTP_ST_CONCLUSAOTX_GABARITO_CHTX_GABARITO_CNTX_GABARITO_LCTX_GABARITO_MT
NU_DESEMPENHO1.0000.2200.9640.7560.7820.7430.7220.7570.7470.7120.7420.8200.7760.2430.2400.2370.2230.0930.2790.1180.2220.1530.2050.0280.0810.1560.1350.0720.1480.0890.2490.1840.1200.1710.1620.1270.2480.1260.0950.1010.1260.1490.1990.0820.0880.1950.2260.0680.0310.5900.6350.6410.5900.6350.8340.0380.6210.1300.5900.6350.5900.635
NU_INFRAESTRUTURA0.2201.0000.2410.2120.2170.1880.1720.1840.1790.1780.2150.2500.1910.3190.3030.3430.3300.2020.5280.1940.4720.4760.4910.1510.2360.3870.4570.2390.4250.1460.5680.4710.2890.4730.5410.2740.4670.4230.1790.2780.1240.2180.1730.0660.0590.2040.2670.0890.0250.1130.1130.1160.1130.1130.1600.0960.1210.1500.1130.1130.1130.113
NU_MEDIA_GERAL0.9640.2411.0000.9180.9120.8870.8890.9030.8930.9010.9110.9360.9240.1430.1400.1680.1590.0500.1500.1110.1730.1170.1590.0250.0640.1230.1070.0570.1170.0710.2720.1430.1310.1850.1270.1400.1970.1500.0550.0620.0820.1050.1520.0980.0700.1090.2540.0770.0270.7020.6810.7510.7020.6810.9920.0490.3970.1460.3510.3200.3310.339
NU_NOTA_CH0.7560.2120.9181.0000.8380.8040.7900.8060.7950.8020.9180.8370.8160.1210.1190.1430.1350.0550.1460.0820.1440.0980.1360.0310.0540.1080.1010.0480.1070.0580.2460.1230.1150.1550.1100.1290.1770.1360.0590.0740.0930.0930.1230.1040.0630.1180.2400.0660.0260.7050.6430.5790.7050.6430.9960.0680.3840.1470.3770.3430.3770.343
NU_NOTA_CN0.7820.2170.9120.8381.0000.7580.7490.7650.7570.7630.8280.8820.7740.1270.1220.1490.1400.0540.1540.0990.1570.1060.1460.0260.0580.1080.1010.0530.1070.0700.2560.1270.1220.1610.1100.1320.1830.1300.0580.0700.0850.0940.1310.1020.0600.1110.2260.0670.0230.6350.7000.5720.6350.7000.8980.1200.3440.1320.3400.3750.3400.375
NU_NOTA_COMP10.7430.1880.8870.8040.7581.0000.8850.9180.9180.8790.8060.7800.9340.1140.1170.1300.1260.0440.1090.0780.1330.0960.1240.0150.0480.0960.0770.0390.0870.0460.1970.1090.0980.1510.1070.0990.1400.1170.0440.0430.0690.0840.1220.0870.0700.1030.1880.0650.0170.6580.6140.5400.6580.6140.9310.1080.3540.1230.3290.2900.3110.306
NU_NOTA_COMP20.7220.1720.8890.7900.7490.8851.0000.9320.9110.9080.7890.7700.9620.1050.1100.1170.1140.0470.1030.0700.1210.0910.1140.0120.0430.0860.0660.0340.0770.0400.1730.0990.0840.1380.1020.0870.1230.1090.0460.0400.0810.0750.1190.0920.0760.1170.1750.0690.0150.6580.6140.5410.6580.6140.9310.0920.3540.1260.3290.3070.3290.307
NU_NOTA_COMP30.7570.1840.9030.8060.7650.9180.9321.0000.9310.9150.8050.7850.9680.1130.1170.1290.1240.0440.1070.0800.1330.0960.1220.0130.0460.0920.0720.0370.0820.0460.1890.1070.0940.1480.1050.0960.1380.1140.0440.0380.0720.0800.1270.0880.0730.1060.1840.0670.0170.6580.6140.5410.6580.6140.9310.0820.3530.1240.3290.2890.3110.306
NU_NOTA_COMP40.7470.1790.8930.7950.7570.9180.9110.9311.0000.9120.7920.7800.9590.1110.1150.1250.1210.0450.1040.0790.1310.0960.1200.0110.0450.0890.0680.0360.0780.0450.1800.1050.0920.1470.1040.0920.1320.1100.0460.0380.0740.0780.1270.0920.0750.1090.1760.0710.0160.6580.6140.5410.6580.6140.9310.0820.3530.1270.3290.2900.3110.306
NU_NOTA_COMP50.7120.1780.9010.8020.7630.8790.9080.9150.9121.0000.7980.7830.9690.1090.1120.1230.1180.0430.1030.0770.1280.0940.1190.0090.0450.0890.0670.0350.0780.0440.1810.1040.0910.1410.1030.0920.1310.1110.0450.0390.0700.0770.1270.0860.0720.1020.1790.0700.0160.5960.5630.4910.5960.5630.8440.0560.3200.1140.2980.2650.2810.281
NU_NOTA_LC0.7420.2150.9110.9180.8280.8060.7890.8050.7920.7981.0000.8300.8140.1230.1220.1450.1390.0550.1450.0800.1430.0980.1350.0320.0540.1130.1040.0500.1100.0580.2490.1250.1160.1570.1140.1270.1730.1430.0610.0780.0900.0960.1180.1010.0640.1180.2410.0680.0260.7060.6430.5800.7060.6430.9990.0440.3850.1430.3780.3440.3780.343
NU_NOTA_MT0.8200.2500.9360.8370.8820.7800.7700.7850.7800.7830.8301.0000.7980.1440.1390.1700.1590.0550.1770.1180.1790.1210.1670.0240.0690.1290.1150.0630.1260.0790.2900.1500.1370.1890.1300.1450.2030.1500.0630.0800.0790.1060.1430.0960.0650.1160.2570.0760.0260.6350.7000.5720.6350.7000.8980.1420.3450.1280.3400.3750.3400.375
NU_NOTA_REDACAO0.7760.1910.9240.8160.7740.9340.9620.9680.9590.9690.8140.7981.0000.1180.1210.1340.1290.0460.1120.0840.1380.1000.1270.0130.0480.0960.0740.0390.0850.0480.1950.1110.0970.1540.1100.0990.1430.1210.0480.0430.0770.0840.1340.0910.0770.1110.1930.0720.0180.6580.6140.5420.6580.6140.9310.0850.3530.1290.3290.2900.3110.307
Q0010.2430.3190.1430.1210.1270.1140.1050.1130.1110.1090.1230.1440.1181.0000.3400.3800.2550.0570.2370.1700.2400.1570.2120.0350.1010.1440.1640.0920.1590.1180.3650.1970.1750.3010.1670.1820.2570.1930.0680.0880.0790.1150.1380.0740.0800.1310.2540.0790.0190.1230.1230.1030.1230.1230.1730.0680.0710.1340.0650.0660.0660.066
Q0020.2400.3030.1400.1190.1220.1170.1100.1170.1150.1120.1220.1390.1210.3401.0000.2430.3450.0580.2130.1510.2180.1520.2030.0180.0910.1320.1510.0810.1470.0950.3230.1750.1390.2730.1570.1520.2330.1940.0740.0800.0910.1070.1330.0840.1000.1530.2360.0810.0180.1330.1330.1120.1330.1330.1880.0720.0770.1420.0710.0710.0710.071
Q0030.2370.3430.1680.1430.1490.1300.1170.1290.1250.1230.1450.1700.1340.3800.2431.0000.4150.0610.3200.2140.2760.1720.2390.0650.1190.1620.1860.1050.1810.1350.3890.2280.1870.3330.1760.1950.2680.2160.0810.1310.0600.1200.1420.0380.0470.1120.2590.0760.0200.1020.1020.0860.1020.1020.1440.0620.0710.1100.0640.0650.0640.065
Q0040.2230.3300.1590.1350.1400.1260.1140.1240.1210.1180.1390.1590.1290.2550.3450.4151.0000.0560.3020.2100.2570.1610.2240.0550.1100.1520.1850.0980.1750.1230.3570.2090.1650.3130.1660.1780.2540.2190.0850.1360.0660.1150.1330.0460.0510.1190.2440.0770.0200.1040.1040.0870.1040.1040.1460.0670.0720.1150.0660.0660.0660.066
Q0050.0930.2020.0500.0550.0540.0440.0470.0440.0450.0430.0550.0550.0460.0570.0580.0610.0561.0000.0560.0460.0920.2080.1230.0630.0710.0630.0740.0450.0740.0450.1320.1150.0770.1080.3320.0710.0880.1050.0340.0410.0640.0510.0670.1090.0680.0730.0790.0830.0120.0810.0810.0680.0810.0810.1150.0440.0420.1310.0410.0360.0380.040
Q0060.2790.5280.1500.1460.1540.1090.1030.1070.1040.1030.1450.1770.1120.2370.2130.3200.3020.0561.0000.2940.3780.2660.3570.0590.1800.2290.2600.1470.2480.1810.5370.3210.2530.4470.2790.2540.3770.3220.0620.1020.0330.1470.1660.0260.0260.0670.2890.0700.0250.1140.1150.0960.1140.1150.1610.1080.0640.1200.0570.0520.0540.058
Q0070.1180.1940.1110.0820.0990.0780.0700.0800.0790.0770.0800.1180.0840.1700.1510.2140.2100.0460.2941.0000.2740.1690.2070.0280.1410.1410.0960.1010.1180.1510.2350.2120.1530.2540.1040.1330.2070.0540.0400.0510.0570.0780.1080.0110.0170.0900.1220.0300.0080.0400.0380.0340.0400.0380.0560.0260.0340.0770.0330.0310.0330.031
Q0080.2220.4720.1730.1440.1570.1330.1210.1330.1310.1280.1430.1790.1380.2400.2180.2760.2570.0920.3780.2741.0000.3480.3110.0470.2400.2190.2060.1250.2110.1680.4540.3270.2370.3850.2320.2300.3030.2520.0750.1120.0690.1330.1410.0260.0290.1260.2300.0630.0180.1010.1010.0850.1010.1010.1430.0680.0760.1160.0710.0720.0710.072
Q0090.1530.4760.1170.0980.1060.0960.0910.0960.0960.0940.0980.1210.1000.1570.1520.1720.1610.2080.2660.1690.3481.0000.2510.0820.1990.1760.1790.1010.1680.0970.3190.2520.1850.2950.2940.1670.2150.2780.0810.1190.0840.0930.0870.0620.0510.1300.1580.0620.0110.0940.0940.0790.0940.0940.1330.0570.0690.1080.0670.0670.0670.067
Q0100.2050.4910.1590.1360.1460.1240.1140.1220.1200.1190.1350.1670.1270.2120.2030.2390.2240.1230.3570.2070.3110.2511.0000.0670.1860.2190.2420.1360.2360.1370.4870.2750.2150.3670.2460.2200.2790.2460.1220.1910.0840.1530.1230.0590.0270.1390.2260.0760.0160.1060.1050.0890.1060.1050.1490.0670.0800.1220.0750.0750.0750.075
Q0110.0280.1510.0250.0310.0260.0150.0120.0130.0110.0090.0320.0240.0130.0350.0180.0650.0550.0630.0590.0280.0470.0820.0671.0000.0550.0490.0770.0890.0700.0880.0440.0420.0370.0530.0810.0450.0360.0770.0560.1040.0280.0300.0300.0270.0160.0390.0500.0300.0070.0180.0190.0160.0180.0190.0250.0480.0150.0360.0140.0140.0140.014
Q0120.0810.2360.0640.0540.0580.0480.0430.0460.0450.0450.0540.0690.0480.1010.0910.1190.1100.0710.1800.1410.2400.1990.1860.0551.0000.3160.1690.1220.1960.1260.2410.2220.1610.2160.1480.1360.1500.2100.0540.0820.0410.0670.0600.0260.0180.0620.1030.0390.0080.0380.0370.0320.0380.0370.0540.0420.0290.0590.0270.0260.0270.026
Q0130.1560.3870.1230.1080.1080.0960.0860.0920.0890.0890.1130.1290.0960.1440.1320.1620.1520.0630.2290.1410.2190.1760.2190.0490.3161.0000.2180.1660.2230.1240.3660.2190.2080.2970.1830.1720.2010.2040.1050.1680.0750.1140.0830.0480.0430.1100.1970.0590.0120.0740.0720.0620.0740.0720.1050.0390.0580.0830.0530.0510.0530.051
Q0140.1350.4570.1070.1010.1010.0770.0660.0720.0680.0670.1040.1150.0740.1640.1510.1860.1850.0740.2600.0960.2060.1790.2420.0770.1690.2181.0000.3360.3030.2370.3880.2070.1640.3000.2010.1840.2200.2830.1280.2300.0590.1280.0820.0520.0120.0860.2050.0720.0150.0570.0540.0470.0570.0540.0800.0740.0450.0740.0410.0390.0410.039
Q0150.0720.2390.0570.0480.0530.0390.0340.0370.0360.0350.0500.0630.0390.0920.0810.1050.0980.0450.1470.1010.1250.1010.1360.0890.1220.1660.3361.0000.2410.3380.2570.1270.1420.2180.1010.0980.1310.1000.0810.1260.0420.0640.0530.0360.0170.0580.0950.0450.0080.0280.0260.0240.0280.0260.0400.0380.0220.0540.0200.0180.0200.018
Q0160.1480.4250.1170.1070.1070.0870.0770.0820.0780.0780.1100.1260.0850.1590.1470.1810.1750.0740.2480.1180.2110.1680.2360.0700.1960.2230.3030.2411.0000.2620.4150.2280.1880.3080.1780.1940.2210.2520.1220.2150.0540.1340.0890.0440.0160.0890.2120.0680.0150.0650.0630.0540.0650.0630.0920.0670.0520.0760.0460.0450.0460.045
Q0170.0890.1460.0710.0580.0700.0460.0400.0460.0450.0440.0580.0790.0480.1180.0950.1350.1230.0450.1810.1510.1680.0970.1370.0880.1260.1240.2370.3380.2621.0000.2340.1310.1420.1830.0680.1200.1530.0520.0430.0660.0310.0610.0690.0120.0080.0490.1000.0290.0090.0290.0280.0250.0290.0280.0410.0400.0220.0480.0210.0200.0210.020
Q0180.2490.5680.2720.2460.2560.1970.1730.1890.1800.1810.2490.2900.1950.3650.3230.3890.3570.1320.5370.2350.4540.3190.4870.0440.2410.3660.3880.2570.4150.2341.0000.4670.2440.3430.3330.2170.4930.1660.2400.3860.1130.2870.2180.0520.0300.1910.2230.0920.0330.1060.1050.1080.1060.1050.1050.0590.1160.1500.1060.1050.1060.105
Q0190.1840.4710.1430.1230.1270.1090.0990.1070.1050.1040.1250.1500.1110.1970.1750.2280.2090.1150.3210.2120.3270.2520.2750.0420.2220.2190.2070.1270.2280.1310.4671.0000.2740.4140.2440.2450.2830.2480.0900.1310.0680.1240.1270.0370.0330.1130.2210.0730.0190.0860.0850.0720.0860.0850.1220.0860.0650.1030.0610.0610.0610.060
Q0200.1200.2890.1310.1150.1220.0980.0840.0940.0920.0910.1160.1370.0970.1750.1390.1870.1650.0770.2530.1530.2370.1850.2150.0370.1610.2080.1640.1420.1880.1420.2440.2741.0000.2100.1920.1660.2640.0750.0970.1450.0870.1100.1060.0420.0370.1220.1040.0510.0120.0590.0600.0620.0590.0600.0590.0280.0610.1020.0590.0600.0590.060
Q0210.1710.4730.1850.1550.1610.1510.1380.1480.1470.1410.1570.1890.1540.3010.2730.3330.3130.1080.4470.2540.3850.2950.3670.0530.2160.2970.3000.2180.3080.1830.3430.4140.2101.0000.3070.2030.3770.1510.1430.2050.1410.1770.1860.0610.0450.2030.1580.0870.0190.0850.0810.0860.0850.0810.0850.0290.0900.1690.0850.0820.0850.082
Q0220.1620.5410.1270.1100.1100.1070.1020.1050.1040.1030.1140.1300.1100.1670.1570.1760.1660.3320.2790.1040.2320.2940.2460.0810.1480.1830.2010.1010.1780.0680.3330.2440.1920.3071.0000.1530.2240.3450.0890.1130.1040.0920.0840.0770.0800.1490.1890.0820.0120.1020.1020.0850.1020.1020.1440.0560.0760.1100.0720.0720.0720.072
Q0230.1270.2740.1400.1290.1320.0990.0870.0960.0920.0920.1270.1450.0990.1820.1520.1950.1780.0710.2540.1330.2300.1670.2200.0450.1360.1720.1840.0980.1940.1200.2170.2450.1660.2030.1531.0000.2480.0890.1080.1900.0470.1180.1130.0140.0250.0840.1130.0400.0160.0550.0560.0570.0550.0560.0550.0320.0590.0550.0550.0560.0550.056
Q0240.2480.4670.1970.1770.1830.1400.1230.1380.1320.1310.1730.2030.1430.2570.2330.2680.2540.0880.3770.2070.3030.2150.2790.0360.1500.2010.2200.1310.2210.1530.4930.2830.2640.3770.2240.2481.0000.2750.0950.1560.0490.1440.1380.0310.0200.0910.2680.0460.0210.0920.0950.0790.0920.0950.1300.1130.0740.0880.0650.0670.0650.067
Q0250.1260.4230.1500.1360.1300.1170.1090.1140.1100.1110.1430.1500.1210.1930.1940.2160.2190.1050.3220.0540.2520.2780.2460.0770.2100.2040.2830.1000.2520.0520.1660.2480.0750.1510.3450.0890.2751.0000.1210.2140.0390.1360.0780.0130.0130.0860.1200.0390.0150.0610.0610.0630.0610.0610.0610.0380.0730.0470.0610.0610.0610.061
SG_UF_ESC0.0950.1790.0550.0590.0580.0440.0460.0440.0460.0450.0610.0630.0480.0680.0740.0810.0850.0340.0620.0400.0750.0810.1220.0560.0540.1050.1280.0810.1220.0430.2400.0900.0970.1430.0890.1080.0950.1211.0000.4820.1190.0970.5230.5250.0590.1210.1530.7120.0210.0740.0740.0640.0740.0740.1040.0770.0430.4410.0380.0350.0360.039
SG_UF_PROVA0.1010.2780.0620.0740.0700.0430.0400.0380.0380.0390.0780.0800.0430.0880.0800.1310.1360.0410.1020.0510.1120.1190.1910.1040.0820.1680.2300.1260.2150.0660.3860.1310.1450.2050.1130.1900.1560.2140.4821.0000.0390.1760.1190.1070.0340.0480.2350.1310.0300.0480.0500.0460.0480.0500.0680.0400.0330.1010.0340.0310.0330.035
TP_ANO_CONCLUIU0.1260.1240.0820.0930.0850.0690.0810.0720.0740.0700.0900.0790.0770.0790.0910.0600.0660.0640.0330.0570.0690.0840.0840.0280.0410.0750.0590.0420.0540.0310.1130.0680.0870.1410.1040.0470.0490.0390.1190.0391.0000.0470.2460.4410.2180.4290.1230.3480.0140.1550.1420.1290.1550.1420.2190.0240.0800.5200.0770.0640.0730.071
TP_COR_RACA0.1490.2180.1050.0930.0940.0840.0750.0800.0780.0770.0960.1060.0840.1150.1070.1200.1150.0510.1470.0780.1330.0930.1530.0300.0670.1140.1280.0640.1340.0610.2870.1240.1100.1770.0920.1180.1440.1360.0970.1760.0471.0000.0740.0340.0420.0770.1720.0540.0340.0670.0660.0560.0670.0660.0950.0200.0470.0710.0420.0420.0420.042
TP_DEPENDENCIA_ADM_ESC0.1990.1730.1520.1230.1310.1220.1190.1270.1270.1270.1180.1430.1340.1380.1330.1420.1330.0670.1660.1080.1410.0870.1230.0300.0600.0830.0820.0530.0890.0690.2180.1270.1060.1860.0840.1130.1380.0780.5230.1190.2460.0741.0000.5250.0580.2620.1330.7130.0120.1000.1050.0860.1000.1050.1410.0750.0780.4410.0710.0740.0710.074
TP_ENSINO0.0820.0660.0980.1040.1020.0870.0920.0880.0920.0860.1010.0960.0910.0740.0840.0380.0460.1090.0260.0110.0260.0620.0590.0270.0260.0480.0520.0360.0440.0120.0520.0370.0420.0610.0770.0140.0310.0130.5250.1070.4410.0340.5251.0000.1120.4840.0500.5240.0100.0960.0950.0980.0960.0950.1360.0420.0980.6830.0960.0950.0960.095
TP_ESTADO_CIVIL0.0880.0590.0700.0630.0600.0700.0760.0730.0750.0720.0640.0650.0770.0800.1000.0470.0510.0680.0260.0170.0290.0510.0270.0160.0180.0430.0120.0170.0160.0080.0300.0330.0370.0450.0800.0250.0200.0130.0590.0340.2180.0420.0580.1121.0000.2760.0960.0810.0130.0880.0840.0720.0880.0840.1240.0180.0650.1240.0620.0590.0620.059
TP_FAIXA_ETARIA0.1950.2040.1090.1180.1110.1030.1170.1060.1090.1020.1180.1160.1110.1310.1530.1120.1190.0730.0670.0900.1260.1300.1390.0390.0620.1100.0860.0580.0890.0490.1910.1130.1220.2030.1490.0840.0910.0860.1210.0480.4290.0770.2620.4840.2761.0000.1980.3620.0130.2140.2030.1790.2140.2030.3020.0350.1100.6130.1070.0910.1010.101
TP_LINGUA0.2260.2670.2540.2400.2260.1880.1750.1840.1760.1790.2410.2570.1930.2540.2360.2590.2440.0790.2890.1220.2300.1580.2260.0500.1030.1970.2050.0950.2120.1000.2230.2210.1040.1580.1890.1130.2680.1200.1530.2350.1230.1720.1330.0500.0960.1981.0000.0670.0350.1020.1020.1050.1020.1020.1020.0960.1120.1450.1020.1020.1020.102
TP_LOCALIZACAO_ESC0.0680.0890.0770.0660.0670.0650.0690.0670.0710.0700.0680.0760.0720.0790.0810.0760.0770.0830.0700.0300.0630.0620.0760.0300.0390.0590.0720.0450.0680.0290.0920.0730.0510.0870.0820.0400.0460.0390.7120.1310.3480.0540.7130.5240.0810.3620.0671.0000.0080.0610.0620.0640.0610.0620.0870.0750.0640.5400.0610.0630.0610.063
TP_NACIONALIDADE0.0310.0250.0270.0260.0230.0170.0150.0170.0160.0160.0260.0260.0180.0190.0180.0200.0200.0120.0250.0080.0180.0110.0160.0070.0080.0120.0150.0080.0150.0090.0330.0190.0120.0190.0120.0160.0210.0150.0210.0300.0140.0340.0120.0100.0130.0130.0350.0081.0000.0080.0090.0080.0080.0090.0110.0210.0080.0150.0050.0060.0050.006
TP_PRESENCA_CH0.5900.1130.7020.7050.6350.6580.6580.6580.6580.5960.7060.6350.6580.1230.1330.1020.1040.0810.1140.0400.1010.0940.1060.0180.0380.0740.0570.0280.0650.0290.1060.0860.0590.0850.1020.0550.0920.0610.0740.0480.1550.0670.1000.0960.0880.2140.1020.0610.0081.0000.6420.7101.0000.6421.0000.0110.7070.1660.7070.6410.7070.641
TP_PRESENCA_CN0.6350.1130.6810.6430.7000.6140.6140.6140.6140.5630.6430.7000.6140.1230.1330.1020.1040.0810.1150.0380.1010.0940.1050.0190.0370.0720.0540.0260.0630.0280.1050.0850.0600.0810.1020.0560.0950.0610.0740.0500.1420.0660.1050.0950.0840.2030.1020.0620.0090.6421.0000.7120.6421.0000.9080.0060.6440.1520.6420.7070.6420.707
TP_PRESENCA_GERAL0.6410.1160.7510.5790.5720.5400.5410.5410.5410.4910.5800.5720.5420.1030.1120.0860.0870.0680.0960.0340.0850.0790.0890.0160.0320.0620.0470.0240.0540.0250.1080.0720.0620.0860.0850.0570.0790.0630.0640.0460.1290.0560.0860.0980.0720.1790.1050.0640.0080.7100.7121.0000.7100.7121.0000.0120.5850.1420.5770.5770.5770.577
TP_PRESENCA_LC0.5900.1130.7020.7050.6350.6580.6580.6580.6580.5960.7060.6350.6580.1230.1330.1020.1040.0810.1140.0400.1010.0940.1060.0180.0380.0740.0570.0280.0650.0290.1060.0860.0590.0850.1020.0550.0920.0610.0740.0480.1550.0670.1000.0960.0880.2140.1020.0610.0081.0000.6420.7101.0000.6421.0000.0110.7070.1660.7070.6410.7070.641
TP_PRESENCA_MT0.6350.1130.6810.6430.7000.6140.6140.6140.6140.5630.6430.7000.6140.1230.1330.1020.1040.0810.1150.0380.1010.0940.1050.0190.0370.0720.0540.0260.0630.0280.1050.0850.0600.0810.1020.0560.0950.0610.0740.0500.1420.0660.1050.0950.0840.2030.1020.0620.0090.6421.0000.7120.6421.0000.9080.0060.6440.1520.6420.7070.6420.707
TP_PRESENCA_REDACAO0.8340.1600.9920.9960.8980.9310.9310.9310.9310.8440.9990.8980.9310.1730.1880.1440.1460.1150.1610.0560.1430.1330.1490.0250.0540.1050.0800.0400.0920.0410.1050.1220.0590.0850.1440.0550.1300.0610.1040.0680.2190.0950.1410.1360.1240.3020.1020.0870.0111.0000.9081.0001.0000.9081.0000.0051.0000.2351.0000.9071.0000.907
TP_SEXO0.0380.0960.0490.0680.1200.1080.0920.0820.0820.0560.0440.1420.0850.0680.0720.0620.0670.0440.1080.0260.0680.0570.0670.0480.0420.0390.0740.0380.0670.0400.0590.0860.0280.0290.0560.0320.1130.0380.0770.0400.0240.0200.0750.0420.0180.0350.0960.0750.0210.0110.0060.0120.0110.0060.0051.0000.0370.0470.0050.0040.0060.002
TP_STATUS_REDACAO0.6210.1210.3970.3840.3440.3540.3540.3530.3530.3200.3850.3450.3530.0710.0770.0710.0720.0420.0640.0340.0760.0690.0800.0150.0290.0580.0450.0220.0520.0220.1160.0650.0610.0900.0760.0590.0740.0730.0430.0330.0800.0470.0780.0980.0650.1100.1120.0640.0080.7070.6440.5850.7070.6441.0000.0371.0000.1390.3540.3220.3540.321
TP_ST_CONCLUSAO0.1300.1500.1460.1470.1320.1230.1260.1240.1270.1140.1430.1280.1290.1340.1420.1100.1150.1310.1200.0770.1160.1080.1220.0360.0590.0830.0740.0540.0760.0480.1500.1030.1020.1690.1100.0550.0880.0470.4410.1010.5200.0710.4410.6830.1240.6130.1450.5400.0150.1660.1520.1420.1660.1520.2350.0470.1391.0000.1360.1240.1360.124
TX_GABARITO_CH0.5900.1130.3510.3770.3400.3290.3290.3290.3290.2980.3780.3400.3290.0650.0710.0640.0660.0410.0570.0330.0710.0670.0750.0140.0270.0530.0410.0200.0460.0210.1060.0610.0590.0850.0720.0550.0650.0610.0380.0340.0770.0420.0710.0960.0620.1070.1020.0610.0050.7070.6420.5770.7070.6421.0000.0050.3540.1361.0000.8341.0000.834
TX_GABARITO_CN0.6350.1130.3200.3430.3750.2900.3070.2890.2900.2650.3440.3750.2900.0660.0710.0650.0660.0360.0520.0310.0720.0670.0750.0140.0260.0510.0390.0180.0450.0200.1050.0610.0600.0820.0720.0560.0670.0610.0350.0310.0640.0420.0740.0950.0590.0910.1020.0630.0060.6410.7070.5770.6410.7070.9070.0040.3220.1240.8341.0000.8501.000
TX_GABARITO_LC0.5900.1130.3310.3770.3400.3110.3290.3110.3110.2810.3780.3400.3110.0660.0710.0640.0660.0380.0540.0330.0710.0670.0750.0140.0270.0530.0410.0200.0460.0210.1060.0610.0590.0850.0720.0550.0650.0610.0360.0330.0730.0420.0710.0960.0620.1010.1020.0610.0050.7070.6420.5770.7070.6421.0000.0060.3540.1361.0000.8501.0000.834
TX_GABARITO_MT0.6350.1130.3390.3430.3750.3060.3070.3060.3060.2810.3430.3750.3070.0660.0710.0650.0660.0400.0580.0310.0720.0670.0750.0140.0260.0510.0390.0180.0450.0200.1050.0600.0600.0820.0720.0560.0670.0610.0390.0350.0710.0420.0740.0950.0590.1010.1020.0630.0060.6410.7070.5770.6410.7070.9070.0020.3210.1240.8341.0000.8341.000

Missing values

2025-04-15T16:03:08.630294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-15T16:03:33.347141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TP_FAIXA_ETARIATP_SEXOTP_ESTADO_CIVILTP_COR_RACATP_NACIONALIDADETP_ST_CONCLUSAOTP_ANO_CONCLUIUTP_ENSINONO_MUNICIPIO_ESCSG_UF_ESCTP_DEPENDENCIA_ADM_ESCTP_LOCALIZACAO_ESCNO_MUNICIPIO_PROVASG_UF_PROVATP_PRESENCA_CNTP_PRESENCA_CHTP_PRESENCA_LCTP_PRESENCA_MTNU_NOTA_CNNU_NOTA_CHNU_NOTA_LCNU_NOTA_MTTX_RESPOSTAS_CNTX_RESPOSTAS_CHTX_RESPOSTAS_LCTX_RESPOSTAS_MTTP_LINGUATX_GABARITO_CNTX_GABARITO_CHTX_GABARITO_LCTX_GABARITO_MTTP_STATUS_REDACAONU_NOTA_COMP1NU_NOTA_COMP2NU_NOTA_COMP3NU_NOTA_COMP4NU_NOTA_COMP5NU_NOTA_REDACAOQ001Q002Q003Q004Q005Q006Q007Q008Q009Q010Q011Q012Q013Q014Q015Q016Q017Q018Q019Q020Q021Q022Q023Q024Q025TP_PRESENCA_GERALTP_PRESENCA_REDACAONU_DESEMPENHONU_MEDIA_GERALNU_INFRAESTRUTURA
014M211117-1.0DesconhecidoDesconhecido-1.0-1.0BrasíliaDF0000-1.0-1.00-1.00-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1AFED5FCCDCDCBBDCCBBABBAAB003-1.01
112M210116-1.0DesconhecidoDesconhecido-1.0-1.0BrasíliaDF0000-1.0-1.00-1.00-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1FEEB3HABCCABBBABABBAACADB003-1.01
26F11110-1.0DesconhecidoDesconhecido-1.0-1.0Caxias do SulRS1111502.0499.00475.50363.25DBEBDCECCBCEBBBBDBABDDBBAABCBACDBACECCBAADEBBABDEADAADCDABDCADAEABCDDCBAADCCBEBCEBEBDBEAEDACEBDCABAACAEBAECEBBBAAECBBDEADCAECCCEDDABEEDCEAEACCCDABCDAACEDDBAAEBABDDEEBDAECABDBCBCADE1DBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEEDACEEABAADCDAADEABCDABCDCABCBDADEBAECABADBCDAEDBABBAEBAAAACDACDEDAACADBADBCCEACCCEAAECBBEBCACEEDBCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC1.0140200100120140700HECF5CABDBABABABAABAAAAAB112508.02
32F131201.0FortalezaCE2.01.0FortalezaCE1111459.0508.50507.25466.75DEEBEACCCEBDDBDCCCAEEDCBAAADBCBEEEDCDAAECBEECDDAAEEBCCDEADBCDDCBAECABEBDEBDABECECEDCDDAEEDADBDADAEEEACAABBACADCAEBBAAEBBCDEBBDDADDCADAAEECBAEDEEDDDBBAADEECDBBBECEAACEAEECDBEDDBCDCB0CDDDABBABDBEABDECCEEEDCEDAEBABDCCAACCCADACDBEDBAADEADCDCABABCDDEBAEABAECABAACECDAECBDAABCDBBBDAABAEACCEEEDEACBCACAACAACAAAECBBEDBCCADBDEDDACEBDADDAEBEACBEDCECCBEABCADEBCCBCCDEBDDAABBADD1.0140200160180200880DDBB5CABBAABAAAAAABAADAAB112564.53
43F131201.0QuixadáCE2.01.0QuixadáCE1111402.5379.25447.00338.25AECCEAACDEABEEECDBAEEAAADDEABCBCEBACEEDCBEABDCADEBCEDDEBCBAEBADDCECACADBDEBABDBDBEEDBBEADCAABBACBCAEDABDADEDAACCAEEEECAACDCADBAEACDEAAECDBABEDCEEBBBDECDEBACCAABDEDCBECDECABBDBDEECC0CAAADCCCCDDDABDCACDBEEEDCEDAEECCDBEABDBABBAEBCDAEECABAACEAADECBDAABCDCABADCDEABAABCDDEBADBBBDABAAEBADACEEDCCDBADBDEDCCEBCACEACAACAACACBBEAAEDCECACCBDECBEEABEABDDAADDABBBCCBCCDDAEBDADEEB1.012012012012080560BBAA4BABAAABAAAAAABAABAAA112425.53
56F13110-1.0DesconhecidoDesconhecido-1.0-1.0IlhéusBA0000-1.0-1.00-1.00-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1EEDB3CABBAABABABAABAACAAB003-1.03
611F131112-1.0DesconhecidoDesconhecido-1.0-1.0PetrolinaPE0000-1.0-1.00-1.00-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1CCBA1CABBAABAAABAABABBAAB003-1.03
711M131112-1.0DesconhecidoDesconhecido-1.0-1.0Governador ValadaresMG0000-1.0-1.00-1.00-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1BBBB1AABCAABABABAAAAABABB003-1.03
85F12111-1.0DesconhecidoDesconhecido-1.0-1.0SalvadorBA0000-1.0-1.00-1.00-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1EECB2CABCAABABABAABAACAAB003-1.02
911M11118-1.0DesconhecidoDesconhecido-1.0-1.0BatataisSP1111564.5630.50610.50680.00CBAACDDEDEDEBCDDBACEACECECCEBBDCBEAEABCDBEADBABCDCBCACDBEACEBAABADAEBAADCBADBCDCEBADCDAADEBDBBADAEDBEEACCAAADEACBBACCAEDCDAEEEBAEEDACCEEBDDECEADDEBCCCCEACDDDCBDBDEAEABABDDCCBDCDACD0CEDAEEDEECCEBADCCCABBABCAACDDDACDBEABDCDBEABDABCDCBDACDAEACEECABADBEABADEBAABCDCABADCDAADEBDBBABAAAEAAECBBEAACAAACEACBCACCCEDEDADBDBEEDDACCCEBDEEBDADDABCCBCCABBADDBDDAEAEABCBEDECADCECCB1.0120120120120120600HEFD2FABCBBBBBABABCBACABB111617.01
TP_FAIXA_ETARIATP_SEXOTP_ESTADO_CIVILTP_COR_RACATP_NACIONALIDADETP_ST_CONCLUSAOTP_ANO_CONCLUIUTP_ENSINONO_MUNICIPIO_ESCSG_UF_ESCTP_DEPENDENCIA_ADM_ESCTP_LOCALIZACAO_ESCNO_MUNICIPIO_PROVASG_UF_PROVATP_PRESENCA_CNTP_PRESENCA_CHTP_PRESENCA_LCTP_PRESENCA_MTNU_NOTA_CNNU_NOTA_CHNU_NOTA_LCNU_NOTA_MTTX_RESPOSTAS_CNTX_RESPOSTAS_CHTX_RESPOSTAS_LCTX_RESPOSTAS_MTTP_LINGUATX_GABARITO_CNTX_GABARITO_CHTX_GABARITO_LCTX_GABARITO_MTTP_STATUS_REDACAONU_NOTA_COMP1NU_NOTA_COMP2NU_NOTA_COMP3NU_NOTA_COMP4NU_NOTA_COMP5NU_NOTA_REDACAOQ001Q002Q003Q004Q005Q006Q007Q008Q009Q010Q011Q012Q013Q014Q015Q016Q017Q018Q019Q020Q021Q022Q023Q024Q025TP_PRESENCA_GERALTP_PRESENCA_REDACAONU_DESEMPENHONU_MEDIA_GERALNU_INFRAESTRUTURA
39339454M12111-1.0DesconhecidoDesconhecido-1.0-1.0Novo HamburgoRS1111469.25553.5571.0578.00BBDBCAAEBEDAEBBDCEAEECCDDABBEACDBEBCDADDACCEEBAABDCEACDCADEBCDAEBCEDAADBBBDDDECAAACCDAABCCBAAEACBEAEDAACBBABCAEBACACADCBBEDBADBEBDBDAACEEEAADBEECCAAADAECEEEBAACDCCBCCBEDEECBDCABCDB1CDDDABBABDBEABDECCEEEDCEDAEBABDCCAACCCADACDBEDBAADEADCDCABABCDDEBAEABAECABAACECDAECBDAABCDBBBDAABAEACCEEEDEACBCACAACAACAAAECBBEDBCCADBDEDDACEBDADDAEBEACBEDCECCBEABCADEBCCBCCDEBDDAABBADD1.012016080120120600HCBB5KABDBBBBCCBABDABEBCB112554.51
393394612F111117-1.0DesconhecidoDesconhecido-1.0-1.0Porto AlegreRS1111568.50605.0598.0496.75DECCEABAEBCDBDDCDEDBEAECCBADEEBADBEABDCCAACBBCDCEBCADCACEACDEBBCAABCDDABEACDECDAEBEDDEBADBBCDABDECEEDCCDBBDBDCDCDEBAACCAEDABACDAECBEAAECDDCDAEBCEDBDABCDEBEAADECADBCDAECBDBDAEEADBED0CAAADCCCCDDDABDCACDBEEEDCEDAEECCDBEABDBABBAEBCDAEECABAACEAADECBDAABCDCABADCDEABAABCDDEBADBBBDABAAEBADACEEDCCDBADBDEDCCEBCACEACAACAACACBBEAAEDCECACCBDECBEEABEABDDAADDABBBCCBCCDDAEBDADEEB1.012012010010060500BBDD3DABDBACABABABCAADABB112553.51
39339479M11115-1.0DesconhecidoDesconhecido-1.0-1.0Rio GrandeRS0000-1.00-1.0-1.0-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1DCBB3DACDBABABABABCAADABB003-1.01
39339483M111201.0São LeopoldoRS2.01.0São LeopoldoRS1111476.25543.0545.0530.50ABBBCEADADBDDDCBCBCECACDAABBBDCAEADEEAEABCACADBAAAEEDCEDEEAEDEACADBCAABCDBDCCECEDDCBDAABCDBCBDBBBAAEDDEABBACBBDABEACAEEBBEABBAABEDEDDACEADAAEDEBBADBBBAABDBCBAABCDCCBDDADCCCDEABDACB0CDDDABBABDBEABDECCEEEDCEDAEBABDCCAACCCADACDBEDBAADEADCDCABABCDDEBAEABAECABAACECDAECBDAABCDBBBDAABAEACCEEEDEACBCACAACAACAAAECBBEDBCCADBDEDDACEBDADDAEBEACBEDCECCBEABCADEBCCBCCDEBDDAABBADD1.012016080120120600EEDD4FACDCABCBBBABDBAEACB112539.01
39339496F13114-1.0DesconhecidoDesconhecido-1.0-1.0IgrejinhaRS0000-1.00-1.0-1.0-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido0DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1EFDD3FABDCABABABABDAADABB003-1.01
393395012M11116-1.0DesconhecidoDesconhecido-1.0-1.0CachoeirinhaRS1111566.00605.5613.5547.50BBEEBBBCCCADEBEEDCBACEEDACDCBEAEBBEEDCABAECABACEDCDAADCAAAEEEBEDABCDCCBCBDAEEAABCABADBDDAAAEABBAAACECDEDAECADBADECEDACCAEAABCBBEBBACEEAAAACEEBADBDDDBBCDEDCACDCEACDDECBCDCADCDEBCDCC0DBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEEDACEEABAADCDAADEABCDABCDCABCBDADEBAECABADBCDAEDBABBAEBAAAACDACDEDAACADBADBCCEACCCEAAECBBEBCACEEDBCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC1.012012012010080540CEFF5FABDBBBABBBAACABEAAB112574.51
39339511F11230-1.0DesconhecidoDesconhecido-1.0-1.0São PauloSP1111377.25535.5610.5644.50ECDDBDBDCDDDBECACBEDDEDABBBADEECBECCACAEEACAEABAAEAAEBCCCAAEDDCDABCDCDBCADABECDAAECADACEDEABABBBACCACDEDAADADBBDCCCBACCBEAAEDBBEBBEBAEDACBCEEBBEADDEEDADECDAAEDCCEBEBAAADEDDCEEEDCCC0DBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEEDACEEABAADCDAADEABCDABCDCABCBDADEBAECABADBCDAEDBABBAEBAAAACDACDEDAACADBADBCCEACCCEAAECBBEBCACEEDBCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC1.0140120120140120640FFBB4FBEDCABBBABABCBBBBCB112561.51
39339523F131201.0DesconhecidoDesconhecido-1.0-1.0FlorianópolisSC0000-1.00-1.0-1.0-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1ECBB6BABCAABAAAAAABAABAAB003-1.03
39339532M111201.0DesconhecidoDesconhecido-1.0-1.0Bragança PaulistaSP1111515.50539.0536.0569.00DCDBEBEDBDBBEDEABBBDCECAEBAECEBCBBDCDCADDECEBADCABBDACACAACAEDDBCDCADDABEBCDBCACEBCEBDCACCCDBBACAEDBBEACDBBBABACBBACCAEDCADACDBAECACBBBCCCCDEBBCADBDDCBCEADAEBCDDEADCCBDBBBCAEEDCBCA0DBEABDABDCACDBECDDDBCAAABBACCCADEBECCCEDAEEEDABCDCBDACDAEACEECABADBEABADEBAABCDCABADCDAADEBDBBABAAAEAAECBBEAACAAACEACBCACCCEDEDADBDBEEDDACCCBCCDEEABCBEDCEABBEBDABDDADDADECAADDCCBEBEABCC1.0120120100120140600GGEE3NBEDCACBBABBBCAADACB112552.01
39339543F131201.0OsascoSP2.01.0OsascoSP0000-1.00-1.0-1.0-1.00DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1EFBE4CABDBBBBBBAAABBADABB003-1.01

Duplicate rows

Most frequently occurring

TP_FAIXA_ETARIATP_SEXOTP_ESTADO_CIVILTP_COR_RACATP_NACIONALIDADETP_ST_CONCLUSAOTP_ANO_CONCLUIUTP_ENSINONO_MUNICIPIO_ESCSG_UF_ESCTP_DEPENDENCIA_ADM_ESCTP_LOCALIZACAO_ESCNO_MUNICIPIO_PROVASG_UF_PROVATP_PRESENCA_CNTP_PRESENCA_CHTP_PRESENCA_LCTP_PRESENCA_MTNU_NOTA_CNNU_NOTA_CHNU_NOTA_LCNU_NOTA_MTTX_RESPOSTAS_CNTX_RESPOSTAS_CHTX_RESPOSTAS_LCTX_RESPOSTAS_MTTP_LINGUATX_GABARITO_CNTX_GABARITO_CHTX_GABARITO_LCTX_GABARITO_MTTP_STATUS_REDACAONU_NOTA_COMP1NU_NOTA_COMP2NU_NOTA_COMP3NU_NOTA_COMP4NU_NOTA_COMP5NU_NOTA_REDACAOQ001Q002Q003Q004Q005Q006Q007Q008Q009Q010Q011Q012Q013Q014Q015Q016Q017Q018Q019Q020Q021Q022Q023Q024Q025TP_PRESENCA_GERALTP_PRESENCA_REDACAONU_DESEMPENHONU_MEDIA_GERALNU_INFRAESTRUTURA# duplicates
8043M131201.0FortalezaCE2.01.0FortalezaCE0000-1.0-1.0-1.0-1.0DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1HHFF3BABBAABAAAAAABAABAAB003-1.0313
8613M131201.0MaranguapeCE2.02.0MaranguapeCE0000-1.0-1.0-1.0-1.0DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1HHFF3BABCAABAAAAAABAACAAA003-1.0313
5623F131201.0MaranguapeCE2.02.0MaranguapeCE0000-1.0-1.0-1.0-1.0DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1HHFF3CABCAABAAAAAABAACAAA003-1.0312
8063M131201.0FortalezaCE2.01.0FortalezaCE0000-1.0-1.0-1.0-1.0DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1HHFF3BABBAABBAAAAABAABAAB003-1.0312
8623M131201.0MaranguapeCE2.02.0MaranguapeCE0000-1.0-1.0-1.0-1.0DesconhecidoDesconhecidoDesconhecidoDesconhecido1DesconhecidoDesconhecidoDesconhecidoDesconhecido-1.0-1-1-1-1-1-1HHFF3CABCAABAAAAAABAACAAA003-1.0312
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